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    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 47: Risk Parity with Risk Factors\n",
    "\n",
    "## 1. Downloading the Data:"
   ]
  },
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     "text": [
      "[*********************100%%**********************]  30 of 30 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',\n",
    "          'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',\n",
    "          'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']\n",
    "assets.sort()\n",
    "\n",
    "# Tickers of factors\n",
    "\n",
    "factors = ['MTUM', 'QUAL', 'VLUE', 'SIZE', 'USMV']\n",
    "factors.sort()\n",
    "\n",
    "tickers = assets + factors\n",
    "tickers.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(tickers, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = tickers"
   ]
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MTUM</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>USMV</th>\n",
       "      <th>VLUE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.4735%</td>\n",
       "      <td>0.2672%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.6780%</td>\n",
       "      <td>0.1634%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.5267%</td>\n",
       "      <td>-1.1914%</td>\n",
       "      <td>-0.5380%</td>\n",
       "      <td>-0.6253%</td>\n",
       "      <td>-1.8277%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-2.2294%</td>\n",
       "      <td>-2.3798%</td>\n",
       "      <td>-1.7181%</td>\n",
       "      <td>-1.6214%</td>\n",
       "      <td>-2.1609%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-0.9548%</td>\n",
       "      <td>-1.1376%</td>\n",
       "      <td>-1.1978%</td>\n",
       "      <td>-1.0086%</td>\n",
       "      <td>-1.0873%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>0.6043%</td>\n",
       "      <td>0.1479%</td>\n",
       "      <td>-0.5898%</td>\n",
       "      <td>0.1491%</td>\n",
       "      <td>-0.6183%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "               MTUM     QUAL     SIZE     USMV     VLUE\n",
       "Date                                                   \n",
       "2016-01-05  0.4735%  0.2672%  0.0000%  0.6780%  0.1634%\n",
       "2016-01-06 -0.5267% -1.1914% -0.5380% -0.6253% -1.8277%\n",
       "2016-01-07 -2.2294% -2.3798% -1.7181% -1.6214% -2.1609%\n",
       "2016-01-08 -0.9548% -1.1376% -1.1978% -1.0086% -1.0873%\n",
       "2016-01-11  0.6043%  0.1479% -0.5898%  0.1491% -0.6183%"
      ]
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   "source": [
    "# Calculating returns\n",
    "\n",
    "X = data[factors].pct_change().dropna()\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(X.head())"
   ]
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   "source": [
    "## 2. Estimating Risk Parity Portfolios with Stepwise Regression\n",
    "\n",
    "### 2.1 Estimating the loadings matrix with Stepwise Regression\n",
    "\n",
    "This part is just to visualize how Riskfolio-Lib calculates a loadings matrix using PCR."
   ]
  },
  {
   "cell_type": "code",
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       "      <th id=\"T_37b42_level0_col1\" class=\"col_heading level0 col1\" >MTUM</th>\n",
       "      <th id=\"T_37b42_level0_col2\" class=\"col_heading level0 col2\" >QUAL</th>\n",
       "      <th id=\"T_37b42_level0_col3\" class=\"col_heading level0 col3\" >SIZE</th>\n",
       "      <th id=\"T_37b42_level0_col4\" class=\"col_heading level0 col4\" >USMV</th>\n",
       "      <th id=\"T_37b42_level0_col5\" class=\"col_heading level0 col5\" >VLUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "      <td id=\"T_37b42_row0_col0\" class=\"data row0 col0\" >-0.0006</td>\n",
       "      <td id=\"T_37b42_row0_col1\" class=\"data row0 col1\" >-0.6551</td>\n",
       "      <td id=\"T_37b42_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row0_col3\" class=\"data row0 col3\" >0.9406</td>\n",
       "      <td id=\"T_37b42_row0_col4\" class=\"data row0 col4\" >-0.7883</td>\n",
       "      <td id=\"T_37b42_row0_col5\" class=\"data row0 col5\" >1.7237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "      <td id=\"T_37b42_row1_col0\" class=\"data row1 col0\" >0.0005</td>\n",
       "      <td id=\"T_37b42_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row1_col2\" class=\"data row1 col2\" >1.1744</td>\n",
       "      <td id=\"T_37b42_row1_col3\" class=\"data row1 col3\" >0.3616</td>\n",
       "      <td id=\"T_37b42_row1_col4\" class=\"data row1 col4\" >-0.4322</td>\n",
       "      <td id=\"T_37b42_row1_col5\" class=\"data row1 col5\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "      <td id=\"T_37b42_row2_col0\" class=\"data row2 col0\" >0.0003</td>\n",
       "      <td id=\"T_37b42_row2_col1\" class=\"data row2 col1\" >0.3146</td>\n",
       "      <td id=\"T_37b42_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row2_col3\" class=\"data row2 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row2_col4\" class=\"data row2 col4\" >0.7717</td>\n",
       "      <td id=\"T_37b42_row2_col5\" class=\"data row2 col5\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "      <td id=\"T_37b42_row3_col0\" class=\"data row3 col0\" >-0.0003</td>\n",
       "      <td id=\"T_37b42_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row3_col2\" class=\"data row3 col2\" >0.8123</td>\n",
       "      <td id=\"T_37b42_row3_col3\" class=\"data row3 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row3_col4\" class=\"data row3 col4\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row3_col5\" class=\"data row3 col5\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "      <td id=\"T_37b42_row4_col0\" class=\"data row4 col0\" >0.0001</td>\n",
       "      <td id=\"T_37b42_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row4_col2\" class=\"data row4 col2\" >0.4958</td>\n",
       "      <td id=\"T_37b42_row4_col3\" class=\"data row4 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row4_col4\" class=\"data row4 col4\" >0.4962</td>\n",
       "      <td id=\"T_37b42_row4_col5\" class=\"data row4 col5\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "      <td id=\"T_37b42_row5_col0\" class=\"data row5 col0\" >0.0001</td>\n",
       "      <td id=\"T_37b42_row5_col1\" class=\"data row5 col1\" >-0.5595</td>\n",
       "      <td id=\"T_37b42_row5_col2\" class=\"data row5 col2\" >-0.2157</td>\n",
       "      <td id=\"T_37b42_row5_col3\" class=\"data row5 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row5_col4\" class=\"data row5 col4\" >1.8341</td>\n",
       "      <td id=\"T_37b42_row5_col5\" class=\"data row5 col5\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "      <td id=\"T_37b42_row6_col0\" class=\"data row6 col0\" >-0.0003</td>\n",
       "      <td id=\"T_37b42_row6_col1\" class=\"data row6 col1\" >-0.4782</td>\n",
       "      <td id=\"T_37b42_row6_col2\" class=\"data row6 col2\" >-0.5994</td>\n",
       "      <td id=\"T_37b42_row6_col3\" class=\"data row6 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row6_col4\" class=\"data row6 col4\" >2.0794</td>\n",
       "      <td id=\"T_37b42_row6_col5\" class=\"data row6 col5\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "      <td id=\"T_37b42_row7_col0\" class=\"data row7 col0\" >0.0004</td>\n",
       "      <td id=\"T_37b42_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row7_col3\" class=\"data row7 col3\" >0.3631</td>\n",
       "      <td id=\"T_37b42_row7_col4\" class=\"data row7 col4\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row7_col5\" class=\"data row7 col5\" >0.8090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "      <td id=\"T_37b42_row8_col0\" class=\"data row8 col0\" >0.0002</td>\n",
       "      <td id=\"T_37b42_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row8_col3\" class=\"data row8 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row8_col4\" class=\"data row8 col4\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row8_col5\" class=\"data row8 col5\" >1.2514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "      <td id=\"T_37b42_row9_col0\" class=\"data row9 col0\" >0.0001</td>\n",
       "      <td id=\"T_37b42_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row9_col2\" class=\"data row9 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row9_col3\" class=\"data row9 col3\" >0.3411</td>\n",
       "      <td id=\"T_37b42_row9_col4\" class=\"data row9 col4\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row9_col5\" class=\"data row9 col5\" >0.5797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "      <td id=\"T_37b42_row10_col0\" class=\"data row10 col0\" >0.0005</td>\n",
       "      <td id=\"T_37b42_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row10_col2\" class=\"data row10 col2\" >0.3076</td>\n",
       "      <td id=\"T_37b42_row10_col3\" class=\"data row10 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row10_col4\" class=\"data row10 col4\" >-0.5485</td>\n",
       "      <td id=\"T_37b42_row10_col5\" class=\"data row10 col5\" >1.1512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "      <td id=\"T_37b42_row11_col0\" class=\"data row11 col0\" >-0.0001</td>\n",
       "      <td id=\"T_37b42_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row11_col2\" class=\"data row11 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row11_col3\" class=\"data row11 col3\" >0.3642</td>\n",
       "      <td id=\"T_37b42_row11_col4\" class=\"data row11 col4\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row11_col5\" class=\"data row11 col5\" >0.6767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "      <td id=\"T_37b42_row12_col0\" class=\"data row12 col0\" >0.0003</td>\n",
       "      <td id=\"T_37b42_row12_col1\" class=\"data row12 col1\" >-0.2693</td>\n",
       "      <td id=\"T_37b42_row12_col2\" class=\"data row12 col2\" >0.6808</td>\n",
       "      <td id=\"T_37b42_row12_col3\" class=\"data row12 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row12_col4\" class=\"data row12 col4\" >0.6066</td>\n",
       "      <td id=\"T_37b42_row12_col5\" class=\"data row12 col5\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "      <td id=\"T_37b42_row13_col0\" class=\"data row13 col0\" >-0.0004</td>\n",
       "      <td id=\"T_37b42_row13_col1\" class=\"data row13 col1\" >-0.4572</td>\n",
       "      <td id=\"T_37b42_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row13_col3\" class=\"data row13 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row13_col4\" class=\"data row13 col4\" >1.4359</td>\n",
       "      <td id=\"T_37b42_row13_col5\" class=\"data row13 col5\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "      <td id=\"T_37b42_row14_col0\" class=\"data row14 col0\" >0.0005</td>\n",
       "      <td id=\"T_37b42_row14_col1\" class=\"data row14 col1\" >1.0302</td>\n",
       "      <td id=\"T_37b42_row14_col2\" class=\"data row14 col2\" >0.6530</td>\n",
       "      <td id=\"T_37b42_row14_col3\" class=\"data row14 col3\" >-0.2640</td>\n",
       "      <td id=\"T_37b42_row14_col4\" class=\"data row14 col4\" >-0.2595</td>\n",
       "      <td id=\"T_37b42_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "      <td id=\"T_37b42_row15_col0\" class=\"data row15 col0\" >-0.0000</td>\n",
       "      <td id=\"T_37b42_row15_col1\" class=\"data row15 col1\" >-0.4648</td>\n",
       "      <td id=\"T_37b42_row15_col2\" class=\"data row15 col2\" >-0.4612</td>\n",
       "      <td id=\"T_37b42_row15_col3\" class=\"data row15 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row15_col4\" class=\"data row15 col4\" >2.3257</td>\n",
       "      <td id=\"T_37b42_row15_col5\" class=\"data row15 col5\" >-0.3532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "      <td id=\"T_37b42_row16_col0\" class=\"data row16 col0\" >0.0003</td>\n",
       "      <td id=\"T_37b42_row16_col1\" class=\"data row16 col1\" >-0.4420</td>\n",
       "      <td id=\"T_37b42_row16_col2\" class=\"data row16 col2\" >1.0319</td>\n",
       "      <td id=\"T_37b42_row16_col3\" class=\"data row16 col3\" >0.2217</td>\n",
       "      <td id=\"T_37b42_row16_col4\" class=\"data row16 col4\" >-0.4831</td>\n",
       "      <td id=\"T_37b42_row16_col5\" class=\"data row16 col5\" >0.7536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "      <td id=\"T_37b42_row17_col0\" class=\"data row17 col0\" >-0.0004</td>\n",
       "      <td id=\"T_37b42_row17_col1\" class=\"data row17 col1\" >-0.3311</td>\n",
       "      <td id=\"T_37b42_row17_col2\" class=\"data row17 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row17_col3\" class=\"data row17 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row17_col4\" class=\"data row17 col4\" >1.7073</td>\n",
       "      <td id=\"T_37b42_row17_col5\" class=\"data row17 col5\" >-0.4915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_37b42_row18_col0\" class=\"data row18 col0\" >-0.0005</td>\n",
       "      <td id=\"T_37b42_row18_col1\" class=\"data row18 col1\" >-0.3267</td>\n",
       "      <td id=\"T_37b42_row18_col2\" class=\"data row18 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row18_col3\" class=\"data row18 col3\" >0.2595</td>\n",
       "      <td id=\"T_37b42_row18_col4\" class=\"data row18 col4\" >0.6973</td>\n",
       "      <td id=\"T_37b42_row18_col5\" class=\"data row18 col5\" >0.4801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "      <td id=\"T_37b42_row19_col0\" class=\"data row19 col0\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row19_col1\" class=\"data row19 col1\" >-0.4414</td>\n",
       "      <td id=\"T_37b42_row19_col2\" class=\"data row19 col2\" >-0.3693</td>\n",
       "      <td id=\"T_37b42_row19_col3\" class=\"data row19 col3\" >-0.2898</td>\n",
       "      <td id=\"T_37b42_row19_col4\" class=\"data row19 col4\" >1.2928</td>\n",
       "      <td id=\"T_37b42_row19_col5\" class=\"data row19 col5\" >0.7562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "      <td id=\"T_37b42_row20_col0\" class=\"data row20 col0\" >0.0005</td>\n",
       "      <td id=\"T_37b42_row20_col1\" class=\"data row20 col1\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row20_col2\" class=\"data row20 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row20_col3\" class=\"data row20 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row20_col4\" class=\"data row20 col4\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row20_col5\" class=\"data row20 col5\" >0.7562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "      <td id=\"T_37b42_row21_col0\" class=\"data row21 col0\" >0.0002</td>\n",
       "      <td id=\"T_37b42_row21_col1\" class=\"data row21 col1\" >0.2867</td>\n",
       "      <td id=\"T_37b42_row21_col2\" class=\"data row21 col2\" >0.5400</td>\n",
       "      <td id=\"T_37b42_row21_col3\" class=\"data row21 col3\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row21_col4\" class=\"data row21 col4\" >0.3864</td>\n",
       "      <td id=\"T_37b42_row21_col5\" class=\"data row21 col5\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "      <td id=\"T_37b42_row22_col0\" class=\"data row22 col0\" >-0.0003</td>\n",
       "      <td id=\"T_37b42_row22_col1\" class=\"data row22 col1\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row22_col2\" class=\"data row22 col2\" >0.4484</td>\n",
       "      <td id=\"T_37b42_row22_col3\" class=\"data row22 col3\" >0.4429</td>\n",
       "      <td id=\"T_37b42_row22_col4\" class=\"data row22 col4\" >-0.6362</td>\n",
       "      <td id=\"T_37b42_row22_col5\" class=\"data row22 col5\" >0.8109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "      <td id=\"T_37b42_row23_col0\" class=\"data row23 col0\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row23_col1\" class=\"data row23 col1\" >-0.5099</td>\n",
       "      <td id=\"T_37b42_row23_col2\" class=\"data row23 col2\" >-0.5270</td>\n",
       "      <td id=\"T_37b42_row23_col3\" class=\"data row23 col3\" >-0.2886</td>\n",
       "      <td id=\"T_37b42_row23_col4\" class=\"data row23 col4\" >1.8940</td>\n",
       "      <td id=\"T_37b42_row23_col5\" class=\"data row23 col5\" >0.4321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_37b42_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "      <td id=\"T_37b42_row24_col0\" class=\"data row24 col0\" >0.0004</td>\n",
       "      <td id=\"T_37b42_row24_col1\" class=\"data row24 col1\" >-0.2371</td>\n",
       "      <td id=\"T_37b42_row24_col2\" class=\"data row24 col2\" >0.0000</td>\n",
       "      <td id=\"T_37b42_row24_col3\" class=\"data row24 col3\" >0.3553</td>\n",
       "      <td id=\"T_37b42_row24_col4\" class=\"data row24 col4\" >-0.8203</td>\n",
       "      <td id=\"T_37b42_row24_col5\" class=\"data row24 col5\" >1.6431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15e5a37d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import riskfolio as rp\n",
    "\n",
    "feature_selection = 'stepwise' # Method to select best model, could be PCR or Stepwise\n",
    "stepwise = 'Forward' # Stepwise Forward regression\n",
    "\n",
    "loadings = rp.loadings_matrix(X=X,\n",
    "                              Y=Y,\n",
    "                              feature_selection=feature_selection,\n",
    "                              stepwise='Forward')\n",
    "\n",
    "loadings.style.format(\"{:.4f}\").background_gradient(cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Calculating the Factor Risk Parity Portfolio based on Market Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>3.4044%</td>\n",
       "      <td>6.7395%</td>\n",
       "      <td>12.1898%</td>\n",
       "      <td>1.7631%</td>\n",
       "      <td>4.7055%</td>\n",
       "      <td>1.3044%</td>\n",
       "      <td>3.9585%</td>\n",
       "      <td>9.1082%</td>\n",
       "      <td>2.6702%</td>\n",
       "      <td>8.1723%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5834%</td>\n",
       "      <td>-4.3735%</td>\n",
       "      <td>4.5495%</td>\n",
       "      <td>4.6026%</td>\n",
       "      <td>-4.8086%</td>\n",
       "      <td>1.6136%</td>\n",
       "      <td>9.9630%</td>\n",
       "      <td>7.0541%</td>\n",
       "      <td>-2.8459%</td>\n",
       "      <td>-0.2024%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA     CNP     CPB      DE  \\\n",
       "weights 3.4044% 6.7395% 12.1898% 1.7631% 4.7055% 1.3044% 3.9585% 9.1082%   \n",
       "\n",
       "            HPQ     JCI  ...      NI     PCAR     PSA     SEE        T  \\\n",
       "weights 2.6702% 8.1723%  ... 5.5834% -4.3735% 4.5495% 4.6026% -4.8086%   \n",
       "\n",
       "            TGT     TMO     TXT       VZ     ZION  \n",
       "weights 1.6136% 9.9630% 7.0541% -2.8459% -0.2024%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Building the portfolio object\n",
    "port = rp.Portfolio(returns=Y)\n",
    "\n",
    "# Calculating optimal portfolio\n",
    "\n",
    "# Select method and estimate input parameters:\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu,\n",
    "                  method_cov=method_cov)\n",
    "\n",
    "feature_selection = 'stepwise' # Method to select best model, could be PCR or Stepwise\n",
    "stepwise = 'Forward' # Forward or Backward regression\n",
    "\n",
    "port.factors = X\n",
    "port.factors_stats(method_mu=method_mu,\n",
    "                   method_cov=method_cov,\n",
    "                   feature_selection=feature_selection,\n",
    "                   stepwise=stepwise,\n",
    "                  )\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model = 'FC' # Factor Contribution Model\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "rf = 0 # Risk free rate\n",
    "b_f = None # Risk factor contribution vector\n",
    "\n",
    "w = port.rp_optimization(model=model,\n",
    "                         rm=rm,\n",
    "                         rf=rf,\n",
    "                         b_f=b_f,\n",
    "                         )\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plotting Portfolio Composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the composition of the portfolio\n",
    "ax = rp.plot_bar(w,\n",
    "                 title='Risk Factor Parity Portfolio - Variance',\n",
    "                 kind=\"v\",\n",
    "                 others=0.05,\n",
    "                 nrow=25,\n",
    "                 height=6,\n",
    "                 width=10,\n",
    "                 ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Calculate the Risk Contribution per Asset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rp.plot_risk_con(w,\n",
    "                      cov=port.cov,\n",
    "                      returns=port.returns,\n",
    "                      rm=rm,\n",
    "                      rf=0,\n",
    "                      alpha=0.05,\n",
    "                      color=\"tab:blue\",\n",
    "                      height=6,\n",
    "                      width=10,\n",
    "                      ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Calculate the Risk Contribution per Risk Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rp.plot_factor_risk_con(\n",
    "    w,\n",
    "    cov=port.cov,\n",
    "    returns=port.returns,\n",
    "    factors=port.factors,\n",
    "    rm=rm,\n",
    "    rf=0,\n",
    "    feature_selection=feature_selection,\n",
    "    stepwise=stepwise,\n",
    "    percentage=True,\n",
    "    erc_line=True,\n",
    "    color=\"tab:orange\",\n",
    "    ax=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estimating Portfolios Using Risk Factors with Other Risk Measures and Stepwise Regression\n",
    "\n",
    "In this part I will calculate optimal factor risk parity portfolios for several risk measures using the market factors matrix as risk factors.\n",
    "\n",
    "### 3.1 Calculate Optimal Factor Risk Parity Portfolios based on Market Factors for Several Risk Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Standard Deviation.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk.\n",
    "# 'WR': Worst Realization (Minimax)\n",
    "# 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).\n",
    "# 'ADD': Average Drawdown of uncompounded cumulative returns.\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM', 'CVaR',\n",
    "       'EVaR', 'CDaR', 'UCI', 'EDaR']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "# When we use hist = True the risk measures all calculated\n",
    "# using historical returns, while when hist = False the\n",
    "# risk measures are calculated using the expected returns \n",
    "#  based on risk factor model: R = a + B * F\n",
    "\n",
    "for i in rms:\n",
    "    w = port.rp_optimization(model=model, rm=i, rf=rf, b_f=b_f)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row1_col3, #T_4139f_row15_col2 {\n",
       "  background-color: #6dc073;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row1_col4 {\n",
       "  background-color: #5db96b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row1_col6 {\n",
       "  background-color: #40aa5c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row1_col7, #T_4139f_row4_col0 {\n",
       "  background-color: #86cc7f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row1_col8 {\n",
       "  background-color: #8dcf81;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row1_col9, #T_4139f_row17_col5, #T_4139f_row18_col5 {\n",
       "  background-color: #7fc97c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row2_col0, #T_4139f_row2_col4 {\n",
       "  background-color: #0b713b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row2_col1 {\n",
       "  background-color: #13773d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row2_col2 {\n",
       "  background-color: #086e3a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row2_col3, #T_4139f_row2_col7, #T_4139f_row2_col9 {\n",
       "  background-color: #187b3f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row2_col5 {\n",
       "  background-color: #056c39;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row2_col6 {\n",
       "  background-color: #005f34;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row2_col8 {\n",
       "  background-color: #1d7f41;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row3_col0, #T_4139f_row3_col2, #T_4139f_row20_col7, #T_4139f_row20_col8 {\n",
       "  background-color: #c0e597;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row3_col1, #T_4139f_row3_col4, #T_4139f_row3_col5, #T_4139f_row5_col5, #T_4139f_row20_col2 {\n",
       "  background-color: #c1e698;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row3_col3, #T_4139f_row3_col7 {\n",
       "  background-color: #c5e89a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row3_col6 {\n",
       "  background-color: #b8e293;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row3_col8 {\n",
       "  background-color: #c7e89a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row4_col1, #T_4139f_row15_col9 {\n",
       "  background-color: #8bce81;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row4_col2, #T_4139f_row15_col1, #T_4139f_row17_col2 {\n",
       "  background-color: #84cb7e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row4_col4, #T_4139f_row15_col7, #T_4139f_row18_col0 {\n",
       "  background-color: #88cd7f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row4_col5, #T_4139f_row18_col2 {\n",
       "  background-color: #83cb7d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row4_col6 {\n",
       "  background-color: #75c578;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row4_col8, #T_4139f_row17_col1, #T_4139f_row18_col1 {\n",
       "  background-color: #97d385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row5_col0, #T_4139f_row5_col4, #T_4139f_row13_col5, #T_4139f_row20_col1 {\n",
       "  background-color: #c8e99b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row5_col1, #T_4139f_row12_col6 {\n",
       "  background-color: #d0ec9f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row5_col6, #T_4139f_row6_col3, #T_4139f_row8_col2, #T_4139f_row8_col5, #T_4139f_row8_col9 {\n",
       "  background-color: #afde8f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row5_col7 {\n",
       "  background-color: #cfec9e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row5_col8, #T_4139f_row13_col9 {\n",
       "  background-color: #d7efa2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row5_col9 {\n",
       "  background-color: #d3eda0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row6_col0, #T_4139f_row6_col4, #T_4139f_row15_col8 {\n",
       "  background-color: #95d385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row6_col1, #T_4139f_row18_col9 {\n",
       "  background-color: #a6da8b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row6_col2, #T_4139f_row15_col3 {\n",
       "  background-color: #90d083;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row6_col5, #T_4139f_row17_col0, #T_4139f_row17_col4, #T_4139f_row18_col4 {\n",
       "  background-color: #89ce80;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row6_col6 {\n",
       "  background-color: #6fc174;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row6_col8 {\n",
       "  background-color: #aedd8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row6_col9, #T_4139f_row17_col8 {\n",
       "  background-color: #a7db8c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row7_col0, #T_4139f_row7_col4, #T_4139f_row21_col7 {\n",
       "  background-color: #339951;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row7_col1 {\n",
       "  background-color: #3ca458;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row7_col2, #T_4139f_row11_col5, #T_4139f_row21_col3 {\n",
       "  background-color: #2f944e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row7_col3 {\n",
       "  background-color: #42ab5d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row7_col5, #T_4139f_row21_col1 {\n",
       "  background-color: #2d914b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row7_col6 {\n",
       "  background-color: #1c7e40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row7_col7, #T_4139f_row9_col1 {\n",
       "  background-color: #4eb163;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row7_col8, #T_4139f_row11_col9 {\n",
       "  background-color: #53b466;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row7_col9, #T_4139f_row22_col5 {\n",
       "  background-color: #4cb063;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row8_col0, #T_4139f_row8_col4 {\n",
       "  background-color: #b1df90;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row8_col1, #T_4139f_row8_col3 {\n",
       "  background-color: #b2df90;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row8_col6, #T_4139f_row18_col8 {\n",
       "  background-color: #acdd8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row8_col7 {\n",
       "  background-color: #abdc8d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row8_col8 {\n",
       "  background-color: #a9db8c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row9_col0 {\n",
       "  background-color: #3fa85b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row9_col2 {\n",
       "  background-color: #3ba358;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row9_col4 {\n",
       "  background-color: #3ea75a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row9_col5, #T_4139f_row22_col6 {\n",
       "  background-color: #379e54;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row9_col6 {\n",
       "  background-color: #268846;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row9_col7, #T_4139f_row22_col1 {\n",
       "  background-color: #61bb6d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row9_col8 {\n",
       "  background-color: #68be71;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row9_col9 {\n",
       "  background-color: #5fba6c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row10_col0, #T_4139f_row10_col1, #T_4139f_row10_col3, #T_4139f_row24_col3, #T_4139f_row24_col7 {\n",
       "  background-color: #e7f6ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row10_col2, #T_4139f_row10_col4, #T_4139f_row10_col5 {\n",
       "  background-color: #e8f6ae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row10_col6, #T_4139f_row24_col8 {\n",
       "  background-color: #e6f5ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row10_col7 {\n",
       "  background-color: #e1f3a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row10_col9 {\n",
       "  background-color: #e3f4aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row11_col0, #T_4139f_row11_col4, #T_4139f_row21_col8 {\n",
       "  background-color: #369d54;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row11_col1 {\n",
       "  background-color: #3fa95c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row11_col2, #T_4139f_row21_col9 {\n",
       "  background-color: #329850;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row11_col3 {\n",
       "  background-color: #4ab062;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row11_col6 {\n",
       "  background-color: #1e8041;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row11_col7, #T_4139f_row22_col0, #T_4139f_row22_col4 {\n",
       "  background-color: #55b567;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row11_col8 {\n",
       "  background-color: #5ab76a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row12_col3, #T_4139f_row24_col2 {\n",
       "  background-color: #ddf2a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row12_col4, #T_4139f_row12_col5 {\n",
       "  background-color: #daf0a4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row12_col7, #T_4139f_row12_col9 {\n",
       "  background-color: #ddf1a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row12_col8, #T_4139f_row24_col0, #T_4139f_row24_col4 {\n",
       "  background-color: #dff3a8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row13_col0, #T_4139f_row13_col4, #T_4139f_row24_col6 {\n",
       "  background-color: #ceeb9e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row13_col1, #T_4139f_row13_col7 {\n",
       "  background-color: #d5eea1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row13_col2, #T_4139f_row20_col3 {\n",
       "  background-color: #cbea9c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row13_col6 {\n",
       "  background-color: #b9e294;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row14_col0, #T_4139f_row14_col1, #T_4139f_row14_col2, #T_4139f_row14_col3, #T_4139f_row14_col4, #T_4139f_row14_col5, #T_4139f_row14_col6, #T_4139f_row14_col7, #T_4139f_row14_col8, #T_4139f_row14_col9 {\n",
       "  background-color: #004529;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row15_col0 {\n",
       "  background-color: #74c477;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row15_col4 {\n",
       "  background-color: #72c376;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row15_col5, #T_4139f_row17_col6 {\n",
       "  background-color: #66bd70;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row15_col6 {\n",
       "  background-color: #49af61;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row16_col0 {\n",
       "  background-color: #feffde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row16_col1 {\n",
       "  background-color: #fdfedd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row16_col2 {\n",
       "  background-color: #feffdf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row16_col3, #T_4139f_row16_col4 {\n",
       "  background-color: #feffe1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row16_col5 {\n",
       "  background-color: #feffe2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row16_col6 {\n",
       "  background-color: #fdfedb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row16_col7, #T_4139f_row16_col8, #T_4139f_row16_col9, #T_4139f_row19_col0, #T_4139f_row19_col1, #T_4139f_row19_col2, #T_4139f_row19_col3, #T_4139f_row19_col4, #T_4139f_row19_col5, #T_4139f_row19_col6 {\n",
       "  background-color: #ffffe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row17_col7 {\n",
       "  background-color: #9cd687;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row17_col9 {\n",
       "  background-color: #9dd688;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row18_col7 {\n",
       "  background-color: #a4d98a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row19_col7, #T_4139f_row19_col8 {\n",
       "  background-color: #fafdcb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row19_col9 {\n",
       "  background-color: #fcfed4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row20_col0, #T_4139f_row20_col4, #T_4139f_row20_col9 {\n",
       "  background-color: #c3e698;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row20_col5 {\n",
       "  background-color: #bee596;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row20_col6 {\n",
       "  background-color: #b5e092;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row21_col0 {\n",
       "  background-color: #298c48;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row21_col2 {\n",
       "  background-color: #288a47;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row21_col4 {\n",
       "  background-color: #2a8d49;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row21_col5 {\n",
       "  background-color: #278946;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row21_col6 {\n",
       "  background-color: #1f8142;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row22_col2 {\n",
       "  background-color: #4fb264;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4139f_row22_col3 {\n",
       "  background-color: #6bc072;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row22_col7 {\n",
       "  background-color: #7ec97b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row22_col8 {\n",
       "  background-color: #81ca7d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row22_col9 {\n",
       "  background-color: #7ac77a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row23_col0, #T_4139f_row23_col4 {\n",
       "  background-color: #f9fdc4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row23_col1 {\n",
       "  background-color: #f9fdc7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row23_col2 {\n",
       "  background-color: #f9fdc2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row23_col3 {\n",
       "  background-color: #fafdc9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row23_col5 {\n",
       "  background-color: #f8fdc1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row23_col6 {\n",
       "  background-color: #f8fcbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row23_col7 {\n",
       "  background-color: #f0f9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row23_col8 {\n",
       "  background-color: #f2fab5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row23_col9 {\n",
       "  background-color: #f6fcb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row24_col1 {\n",
       "  background-color: #e4f4ab;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row24_col5 {\n",
       "  background-color: #dcf1a5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4139f_row24_col9 {\n",
       "  background-color: #e9f6af;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4139f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4139f_level0_col0\" class=\"col_heading level0 col0\" >MV</th>\n",
       "      <th id=\"T_4139f_level0_col1\" class=\"col_heading level0 col1\" >MAD</th>\n",
       "      <th id=\"T_4139f_level0_col2\" class=\"col_heading level0 col2\" >MSV</th>\n",
       "      <th id=\"T_4139f_level0_col3\" class=\"col_heading level0 col3\" >FLPM</th>\n",
       "      <th id=\"T_4139f_level0_col4\" class=\"col_heading level0 col4\" >SLPM</th>\n",
       "      <th id=\"T_4139f_level0_col5\" class=\"col_heading level0 col5\" >CVaR</th>\n",
       "      <th id=\"T_4139f_level0_col6\" class=\"col_heading level0 col6\" >EVaR</th>\n",
       "      <th id=\"T_4139f_level0_col7\" class=\"col_heading level0 col7\" >CDaR</th>\n",
       "      <th id=\"T_4139f_level0_col8\" class=\"col_heading level0 col8\" >UCI</th>\n",
       "      <th id=\"T_4139f_level0_col9\" class=\"col_heading level0 col9\" >EDaR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "      <td id=\"T_4139f_row0_col0\" class=\"data row0 col0\" >3.40%</td>\n",
       "      <td id=\"T_4139f_row0_col1\" class=\"data row0 col1\" >2.78%</td>\n",
       "      <td id=\"T_4139f_row0_col2\" class=\"data row0 col2\" >3.63%</td>\n",
       "      <td id=\"T_4139f_row0_col3\" class=\"data row0 col3\" >2.38%</td>\n",
       "      <td id=\"T_4139f_row0_col4\" class=\"data row0 col4\" >3.36%</td>\n",
       "      <td id=\"T_4139f_row0_col5\" class=\"data row0 col5\" >3.86%</td>\n",
       "      <td id=\"T_4139f_row0_col6\" class=\"data row0 col6\" >5.31%</td>\n",
       "      <td id=\"T_4139f_row0_col7\" class=\"data row0 col7\" >0.29%</td>\n",
       "      <td id=\"T_4139f_row0_col8\" class=\"data row0 col8\" >0.13%</td>\n",
       "      <td id=\"T_4139f_row0_col9\" class=\"data row0 col9\" >0.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "      <td id=\"T_4139f_row1_col0\" class=\"data row1 col0\" >6.74%</td>\n",
       "      <td id=\"T_4139f_row1_col1\" class=\"data row1 col1\" >6.67%</td>\n",
       "      <td id=\"T_4139f_row1_col2\" class=\"data row1 col2\" >6.78%</td>\n",
       "      <td id=\"T_4139f_row1_col3\" class=\"data row1 col3\" >6.50%</td>\n",
       "      <td id=\"T_4139f_row1_col4\" class=\"data row1 col4\" >6.68%</td>\n",
       "      <td id=\"T_4139f_row1_col5\" class=\"data row1 col5\" >6.69%</td>\n",
       "      <td id=\"T_4139f_row1_col6\" class=\"data row1 col6\" >7.29%</td>\n",
       "      <td id=\"T_4139f_row1_col7\" class=\"data row1 col7\" >5.65%</td>\n",
       "      <td id=\"T_4139f_row1_col8\" class=\"data row1 col8\" >5.59%</td>\n",
       "      <td id=\"T_4139f_row1_col9\" class=\"data row1 col9\" >6.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "      <td id=\"T_4139f_row2_col0\" class=\"data row2 col0\" >12.19%</td>\n",
       "      <td id=\"T_4139f_row2_col1\" class=\"data row2 col1\" >11.69%</td>\n",
       "      <td id=\"T_4139f_row2_col2\" class=\"data row2 col2\" >12.44%</td>\n",
       "      <td id=\"T_4139f_row2_col3\" class=\"data row2 col3\" >11.59%</td>\n",
       "      <td id=\"T_4139f_row2_col4\" class=\"data row2 col4\" >12.40%</td>\n",
       "      <td id=\"T_4139f_row2_col5\" class=\"data row2 col5\" >12.70%</td>\n",
       "      <td id=\"T_4139f_row2_col6\" class=\"data row2 col6\" >13.70%</td>\n",
       "      <td id=\"T_4139f_row2_col7\" class=\"data row2 col7\" >13.38%</td>\n",
       "      <td id=\"T_4139f_row2_col8\" class=\"data row2 col8\" >13.46%</td>\n",
       "      <td id=\"T_4139f_row2_col9\" class=\"data row2 col9\" >13.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "      <td id=\"T_4139f_row3_col0\" class=\"data row3 col0\" >1.76%</td>\n",
       "      <td id=\"T_4139f_row3_col1\" class=\"data row3 col1\" >2.17%</td>\n",
       "      <td id=\"T_4139f_row3_col2\" class=\"data row3 col2\" >1.60%</td>\n",
       "      <td id=\"T_4139f_row3_col3\" class=\"data row3 col3\" >2.28%</td>\n",
       "      <td id=\"T_4139f_row3_col4\" class=\"data row3 col4\" >1.66%</td>\n",
       "      <td id=\"T_4139f_row3_col5\" class=\"data row3 col5\" >1.35%</td>\n",
       "      <td id=\"T_4139f_row3_col6\" class=\"data row3 col6\" >0.72%</td>\n",
       "      <td id=\"T_4139f_row3_col7\" class=\"data row3 col7\" >1.83%</td>\n",
       "      <td id=\"T_4139f_row3_col8\" class=\"data row3 col8\" >1.89%</td>\n",
       "      <td id=\"T_4139f_row3_col9\" class=\"data row3 col9\" >1.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "      <td id=\"T_4139f_row4_col0\" class=\"data row4 col0\" >4.71%</td>\n",
       "      <td id=\"T_4139f_row4_col1\" class=\"data row4 col1\" >4.83%</td>\n",
       "      <td id=\"T_4139f_row4_col2\" class=\"data row4 col2\" >4.66%</td>\n",
       "      <td id=\"T_4139f_row4_col3\" class=\"data row4 col3\" >4.87%</td>\n",
       "      <td id=\"T_4139f_row4_col4\" class=\"data row4 col4\" >4.69%</td>\n",
       "      <td id=\"T_4139f_row4_col5\" class=\"data row4 col5\" >4.58%</td>\n",
       "      <td id=\"T_4139f_row4_col6\" class=\"data row4 col6\" >4.43%</td>\n",
       "      <td id=\"T_4139f_row4_col7\" class=\"data row4 col7\" >5.04%</td>\n",
       "      <td id=\"T_4139f_row4_col8\" class=\"data row4 col8\" >5.05%</td>\n",
       "      <td id=\"T_4139f_row4_col9\" class=\"data row4 col9\" >5.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "      <td id=\"T_4139f_row5_col0\" class=\"data row5 col0\" >1.30%</td>\n",
       "      <td id=\"T_4139f_row5_col1\" class=\"data row5 col1\" >1.31%</td>\n",
       "      <td id=\"T_4139f_row5_col2\" class=\"data row5 col2\" >1.30%</td>\n",
       "      <td id=\"T_4139f_row5_col3\" class=\"data row5 col3\" >1.25%</td>\n",
       "      <td id=\"T_4139f_row5_col4\" class=\"data row5 col4\" >1.28%</td>\n",
       "      <td id=\"T_4139f_row5_col5\" class=\"data row5 col5\" >1.32%</td>\n",
       "      <td id=\"T_4139f_row5_col6\" class=\"data row5 col6\" >1.19%</td>\n",
       "      <td id=\"T_4139f_row5_col7\" class=\"data row5 col7\" >1.09%</td>\n",
       "      <td id=\"T_4139f_row5_col8\" class=\"data row5 col8\" >0.73%</td>\n",
       "      <td id=\"T_4139f_row5_col9\" class=\"data row5 col9\" >0.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "      <td id=\"T_4139f_row6_col0\" class=\"data row6 col0\" >3.96%</td>\n",
       "      <td id=\"T_4139f_row6_col1\" class=\"data row6 col1\" >3.63%</td>\n",
       "      <td id=\"T_4139f_row6_col2\" class=\"data row6 col2\" >4.10%</td>\n",
       "      <td id=\"T_4139f_row6_col3\" class=\"data row6 col3\" >3.49%</td>\n",
       "      <td id=\"T_4139f_row6_col4\" class=\"data row6 col4\" >4.04%</td>\n",
       "      <td id=\"T_4139f_row6_col5\" class=\"data row6 col5\" >4.31%</td>\n",
       "      <td id=\"T_4139f_row6_col6\" class=\"data row6 col6\" >4.76%</td>\n",
       "      <td id=\"T_4139f_row6_col7\" class=\"data row6 col7\" >4.05%</td>\n",
       "      <td id=\"T_4139f_row6_col8\" class=\"data row6 col8\" >3.67%</td>\n",
       "      <td id=\"T_4139f_row6_col9\" class=\"data row6 col9\" >3.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "      <td id=\"T_4139f_row7_col0\" class=\"data row7 col0\" >9.11%</td>\n",
       "      <td id=\"T_4139f_row7_col1\" class=\"data row7 col1\" >8.58%</td>\n",
       "      <td id=\"T_4139f_row7_col2\" class=\"data row7 col2\" >9.34%</td>\n",
       "      <td id=\"T_4139f_row7_col3\" class=\"data row7 col3\" >8.40%</td>\n",
       "      <td id=\"T_4139f_row7_col4\" class=\"data row7 col4\" >9.23%</td>\n",
       "      <td id=\"T_4139f_row7_col5\" class=\"data row7 col5\" >9.58%</td>\n",
       "      <td id=\"T_4139f_row7_col6\" class=\"data row7 col6\" >10.69%</td>\n",
       "      <td id=\"T_4139f_row7_col7\" class=\"data row7 col7\" >8.78%</td>\n",
       "      <td id=\"T_4139f_row7_col8\" class=\"data row7 col8\" >8.84%</td>\n",
       "      <td id=\"T_4139f_row7_col9\" class=\"data row7 col9\" >8.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "      <td id=\"T_4139f_row8_col0\" class=\"data row8 col0\" >2.67%</td>\n",
       "      <td id=\"T_4139f_row8_col1\" class=\"data row8 col1\" >3.04%</td>\n",
       "      <td id=\"T_4139f_row8_col2\" class=\"data row8 col2\" >2.51%</td>\n",
       "      <td id=\"T_4139f_row8_col3\" class=\"data row8 col3\" >3.31%</td>\n",
       "      <td id=\"T_4139f_row8_col4\" class=\"data row8 col4\" >2.65%</td>\n",
       "      <td id=\"T_4139f_row8_col5\" class=\"data row8 col5\" >2.39%</td>\n",
       "      <td id=\"T_4139f_row8_col6\" class=\"data row8 col6\" >1.44%</td>\n",
       "      <td id=\"T_4139f_row8_col7\" class=\"data row8 col7\" >3.60%</td>\n",
       "      <td id=\"T_4139f_row8_col8\" class=\"data row8 col8\" >3.97%</td>\n",
       "      <td id=\"T_4139f_row8_col9\" class=\"data row8 col9\" >3.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "      <td id=\"T_4139f_row9_col0\" class=\"data row9 col0\" >8.17%</td>\n",
       "      <td id=\"T_4139f_row9_col1\" class=\"data row9 col1\" >7.63%</td>\n",
       "      <td id=\"T_4139f_row9_col2\" class=\"data row9 col2\" >8.41%</td>\n",
       "      <td id=\"T_4139f_row9_col3\" class=\"data row9 col3\" >7.42%</td>\n",
       "      <td id=\"T_4139f_row9_col4\" class=\"data row9 col4\" >8.29%</td>\n",
       "      <td id=\"T_4139f_row9_col5\" class=\"data row9 col5\" >8.66%</td>\n",
       "      <td id=\"T_4139f_row9_col6\" class=\"data row9 col6\" >9.83%</td>\n",
       "      <td id=\"T_4139f_row9_col7\" class=\"data row9 col7\" >7.73%</td>\n",
       "      <td id=\"T_4139f_row9_col8\" class=\"data row9 col8\" >7.74%</td>\n",
       "      <td id=\"T_4139f_row9_col9\" class=\"data row9 col9\" >7.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "      <td id=\"T_4139f_row10_col0\" class=\"data row10 col0\" >-0.89%</td>\n",
       "      <td id=\"T_4139f_row10_col1\" class=\"data row10 col1\" >-0.27%</td>\n",
       "      <td id=\"T_4139f_row10_col2\" class=\"data row10 col2\" >-1.17%</td>\n",
       "      <td id=\"T_4139f_row10_col3\" class=\"data row10 col3\" >0.06%</td>\n",
       "      <td id=\"T_4139f_row10_col4\" class=\"data row10 col4\" >-1.00%</td>\n",
       "      <td id=\"T_4139f_row10_col5\" class=\"data row10 col5\" >-1.44%</td>\n",
       "      <td id=\"T_4139f_row10_col6\" class=\"data row10 col6\" >-2.80%</td>\n",
       "      <td id=\"T_4139f_row10_col7\" class=\"data row10 col7\" >-0.34%</td>\n",
       "      <td id=\"T_4139f_row10_col8\" class=\"data row10 col8\" >0.06%</td>\n",
       "      <td id=\"T_4139f_row10_col9\" class=\"data row10 col9\" >-0.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "      <td id=\"T_4139f_row11_col0\" class=\"data row11 col0\" >8.85%</td>\n",
       "      <td id=\"T_4139f_row11_col1\" class=\"data row11 col1\" >8.28%</td>\n",
       "      <td id=\"T_4139f_row11_col2\" class=\"data row11 col2\" >9.09%</td>\n",
       "      <td id=\"T_4139f_row11_col3\" class=\"data row11 col3\" >8.07%</td>\n",
       "      <td id=\"T_4139f_row11_col4\" class=\"data row11 col4\" >8.97%</td>\n",
       "      <td id=\"T_4139f_row11_col5\" class=\"data row11 col5\" >9.35%</td>\n",
       "      <td id=\"T_4139f_row11_col6\" class=\"data row11 col6\" >10.56%</td>\n",
       "      <td id=\"T_4139f_row11_col7\" class=\"data row11 col7\" >8.42%</td>\n",
       "      <td id=\"T_4139f_row11_col8\" class=\"data row11 col8\" >8.44%</td>\n",
       "      <td id=\"T_4139f_row11_col9\" class=\"data row11 col9\" >8.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "      <td id=\"T_4139f_row12_col0\" class=\"data row12 col0\" >0.31%</td>\n",
       "      <td id=\"T_4139f_row12_col1\" class=\"data row12 col1\" >0.77%</td>\n",
       "      <td id=\"T_4139f_row12_col2\" class=\"data row12 col2\" >0.11%</td>\n",
       "      <td id=\"T_4139f_row12_col3\" class=\"data row12 col3\" >0.87%</td>\n",
       "      <td id=\"T_4139f_row12_col4\" class=\"data row12 col4\" >0.18%</td>\n",
       "      <td id=\"T_4139f_row12_col5\" class=\"data row12 col5\" >-0.14%</td>\n",
       "      <td id=\"T_4139f_row12_col6\" class=\"data row12 col6\" >-0.94%</td>\n",
       "      <td id=\"T_4139f_row12_col7\" class=\"data row12 col7\" >0.11%</td>\n",
       "      <td id=\"T_4139f_row12_col8\" class=\"data row12 col8\" >0.01%</td>\n",
       "      <td id=\"T_4139f_row12_col9\" class=\"data row12 col9\" >0.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "      <td id=\"T_4139f_row13_col0\" class=\"data row13 col0\" >0.99%</td>\n",
       "      <td id=\"T_4139f_row13_col1\" class=\"data row13 col1\" >1.09%</td>\n",
       "      <td id=\"T_4139f_row13_col2\" class=\"data row13 col2\" >0.94%</td>\n",
       "      <td id=\"T_4139f_row13_col3\" class=\"data row13 col3\" >1.07%</td>\n",
       "      <td id=\"T_4139f_row13_col4\" class=\"data row13 col4\" >0.94%</td>\n",
       "      <td id=\"T_4139f_row13_col5\" class=\"data row13 col5\" >0.90%</td>\n",
       "      <td id=\"T_4139f_row13_col6\" class=\"data row13 col6\" >0.63%</td>\n",
       "      <td id=\"T_4139f_row13_col7\" class=\"data row13 col7\" >0.79%</td>\n",
       "      <td id=\"T_4139f_row13_col8\" class=\"data row13 col8\" >0.51%</td>\n",
       "      <td id=\"T_4139f_row13_col9\" class=\"data row13 col9\" >0.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "      <td id=\"T_4139f_row14_col0\" class=\"data row14 col0\" >15.59%</td>\n",
       "      <td id=\"T_4139f_row14_col1\" class=\"data row14 col1\" >15.50%</td>\n",
       "      <td id=\"T_4139f_row14_col2\" class=\"data row14 col2\" >15.68%</td>\n",
       "      <td id=\"T_4139f_row14_col3\" class=\"data row14 col3\" >15.71%</td>\n",
       "      <td id=\"T_4139f_row14_col4\" class=\"data row14 col4\" >15.82%</td>\n",
       "      <td id=\"T_4139f_row14_col5\" class=\"data row14 col5\" >15.74%</td>\n",
       "      <td id=\"T_4139f_row14_col6\" class=\"data row14 col6\" >15.97%</td>\n",
       "      <td id=\"T_4139f_row14_col7\" class=\"data row14 col7\" >18.55%</td>\n",
       "      <td id=\"T_4139f_row14_col8\" class=\"data row14 col8\" >19.15%</td>\n",
       "      <td id=\"T_4139f_row14_col9\" class=\"data row14 col9\" >18.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "      <td id=\"T_4139f_row15_col0\" class=\"data row15 col0\" >5.58%</td>\n",
       "      <td id=\"T_4139f_row15_col1\" class=\"data row15 col1\" >5.15%</td>\n",
       "      <td id=\"T_4139f_row15_col2\" class=\"data row15 col2\" >5.78%</td>\n",
       "      <td id=\"T_4139f_row15_col3\" class=\"data row15 col3\" >4.93%</td>\n",
       "      <td id=\"T_4139f_row15_col4\" class=\"data row15 col4\" >5.68%</td>\n",
       "      <td id=\"T_4139f_row15_col5\" class=\"data row15 col5\" >6.02%</td>\n",
       "      <td id=\"T_4139f_row15_col6\" class=\"data row15 col6\" >6.77%</td>\n",
       "      <td id=\"T_4139f_row15_col7\" class=\"data row15 col7\" >5.60%</td>\n",
       "      <td id=\"T_4139f_row15_col8\" class=\"data row15 col8\" >5.12%</td>\n",
       "      <td id=\"T_4139f_row15_col9\" class=\"data row15 col9\" >5.51%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "      <td id=\"T_4139f_row16_col0\" class=\"data row16 col0\" >-4.37%</td>\n",
       "      <td id=\"T_4139f_row16_col1\" class=\"data row16 col1\" >-3.55%</td>\n",
       "      <td id=\"T_4139f_row16_col2\" class=\"data row16 col2\" >-4.76%</td>\n",
       "      <td id=\"T_4139f_row16_col3\" class=\"data row16 col3\" >-3.36%</td>\n",
       "      <td id=\"T_4139f_row16_col4\" class=\"data row16 col4\" >-4.67%</td>\n",
       "      <td id=\"T_4139f_row16_col5\" class=\"data row16 col5\" >-5.22%</td>\n",
       "      <td id=\"T_4139f_row16_col6\" class=\"data row16 col6\" >-6.67%</td>\n",
       "      <td id=\"T_4139f_row16_col7\" class=\"data row16 col7\" >-5.61%</td>\n",
       "      <td id=\"T_4139f_row16_col8\" class=\"data row16 col8\" >-5.57%</td>\n",
       "      <td id=\"T_4139f_row16_col9\" class=\"data row16 col9\" >-5.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "      <td id=\"T_4139f_row17_col0\" class=\"data row17 col0\" >4.55%</td>\n",
       "      <td id=\"T_4139f_row17_col1\" class=\"data row17 col1\" >4.32%</td>\n",
       "      <td id=\"T_4139f_row17_col2\" class=\"data row17 col2\" >4.66%</td>\n",
       "      <td id=\"T_4139f_row17_col3\" class=\"data row17 col3\" >4.16%</td>\n",
       "      <td id=\"T_4139f_row17_col4\" class=\"data row17 col4\" >4.59%</td>\n",
       "      <td id=\"T_4139f_row17_col5\" class=\"data row17 col5\" >4.76%</td>\n",
       "      <td id=\"T_4139f_row17_col6\" class=\"data row17 col6\" >5.25%</td>\n",
       "      <td id=\"T_4139f_row17_col7\" class=\"data row17 col7\" >4.44%</td>\n",
       "      <td id=\"T_4139f_row17_col8\" class=\"data row17 col8\" >4.05%</td>\n",
       "      <td id=\"T_4139f_row17_col9\" class=\"data row17 col9\" >4.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_4139f_row18_col0\" class=\"data row18 col0\" >4.60%</td>\n",
       "      <td id=\"T_4139f_row18_col1\" class=\"data row18 col1\" >4.35%</td>\n",
       "      <td id=\"T_4139f_row18_col2\" class=\"data row18 col2\" >4.70%</td>\n",
       "      <td id=\"T_4139f_row18_col3\" class=\"data row18 col3\" >4.21%</td>\n",
       "      <td id=\"T_4139f_row18_col4\" class=\"data row18 col4\" >4.62%</td>\n",
       "      <td id=\"T_4139f_row18_col5\" class=\"data row18 col5\" >4.81%</td>\n",
       "      <td id=\"T_4139f_row18_col6\" class=\"data row18 col6\" >5.33%</td>\n",
       "      <td id=\"T_4139f_row18_col7\" class=\"data row18 col7\" >3.96%</td>\n",
       "      <td id=\"T_4139f_row18_col8\" class=\"data row18 col8\" >3.80%</td>\n",
       "      <td id=\"T_4139f_row18_col9\" class=\"data row18 col9\" >4.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "      <td id=\"T_4139f_row19_col0\" class=\"data row19 col0\" >-4.81%</td>\n",
       "      <td id=\"T_4139f_row19_col1\" class=\"data row19 col1\" >-4.04%</td>\n",
       "      <td id=\"T_4139f_row19_col2\" class=\"data row19 col2\" >-5.16%</td>\n",
       "      <td id=\"T_4139f_row19_col3\" class=\"data row19 col3\" >-3.65%</td>\n",
       "      <td id=\"T_4139f_row19_col4\" class=\"data row19 col4\" >-4.94%</td>\n",
       "      <td id=\"T_4139f_row19_col5\" class=\"data row19 col5\" >-5.42%</td>\n",
       "      <td id=\"T_4139f_row19_col6\" class=\"data row19 col6\" >-7.39%</td>\n",
       "      <td id=\"T_4139f_row19_col7\" class=\"data row19 col7\" >-3.79%</td>\n",
       "      <td id=\"T_4139f_row19_col8\" class=\"data row19 col8\" >-3.71%</td>\n",
       "      <td id=\"T_4139f_row19_col9\" class=\"data row19 col9\" >-4.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "      <td id=\"T_4139f_row20_col0\" class=\"data row20 col0\" >1.61%</td>\n",
       "      <td id=\"T_4139f_row20_col1\" class=\"data row20 col1\" >1.83%</td>\n",
       "      <td id=\"T_4139f_row20_col2\" class=\"data row20 col2\" >1.52%</td>\n",
       "      <td id=\"T_4139f_row20_col3\" class=\"data row20 col3\" >2.00%</td>\n",
       "      <td id=\"T_4139f_row20_col4\" class=\"data row20 col4\" >1.60%</td>\n",
       "      <td id=\"T_4139f_row20_col5\" class=\"data row20 col5\" >1.44%</td>\n",
       "      <td id=\"T_4139f_row20_col6\" class=\"data row20 col6\" >0.87%</td>\n",
       "      <td id=\"T_4139f_row20_col7\" class=\"data row20 col7\" >2.17%</td>\n",
       "      <td id=\"T_4139f_row20_col8\" class=\"data row20 col8\" >2.40%</td>\n",
       "      <td id=\"T_4139f_row20_col9\" class=\"data row20 col9\" >2.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "      <td id=\"T_4139f_row21_col0\" class=\"data row21 col0\" >9.96%</td>\n",
       "      <td id=\"T_4139f_row21_col1\" class=\"data row21 col1\" >9.85%</td>\n",
       "      <td id=\"T_4139f_row21_col2\" class=\"data row21 col2\" >10.04%</td>\n",
       "      <td id=\"T_4139f_row21_col3\" class=\"data row21 col3\" >9.86%</td>\n",
       "      <td id=\"T_4139f_row21_col4\" class=\"data row21 col4\" >10.06%</td>\n",
       "      <td id=\"T_4139f_row21_col5\" class=\"data row21 col5\" >10.07%</td>\n",
       "      <td id=\"T_4139f_row21_col6\" class=\"data row21 col6\" >10.42%</td>\n",
       "      <td id=\"T_4139f_row21_col7\" class=\"data row21 col7\" >10.90%</td>\n",
       "      <td id=\"T_4139f_row21_col8\" class=\"data row21 col8\" >11.03%</td>\n",
       "      <td id=\"T_4139f_row21_col9\" class=\"data row21 col9\" >11.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "      <td id=\"T_4139f_row22_col0\" class=\"data row22 col0\" >7.05%</td>\n",
       "      <td id=\"T_4139f_row22_col1\" class=\"data row22 col1\" >6.74%</td>\n",
       "      <td id=\"T_4139f_row22_col2\" class=\"data row22 col2\" >7.19%</td>\n",
       "      <td id=\"T_4139f_row22_col3\" class=\"data row22 col3\" >6.58%</td>\n",
       "      <td id=\"T_4139f_row22_col4\" class=\"data row22 col4\" >7.08%</td>\n",
       "      <td id=\"T_4139f_row22_col5\" class=\"data row22 col5\" >7.28%</td>\n",
       "      <td id=\"T_4139f_row22_col6\" class=\"data row22 col6\" >8.12%</td>\n",
       "      <td id=\"T_4139f_row22_col7\" class=\"data row22 col7\" >6.18%</td>\n",
       "      <td id=\"T_4139f_row22_col8\" class=\"data row22 col8\" >6.28%</td>\n",
       "      <td id=\"T_4139f_row22_col9\" class=\"data row22 col9\" >6.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "      <td id=\"T_4139f_row23_col0\" class=\"data row23 col0\" >-2.85%</td>\n",
       "      <td id=\"T_4139f_row23_col1\" class=\"data row23 col1\" >-2.33%</td>\n",
       "      <td id=\"T_4139f_row23_col2\" class=\"data row23 col2\" >-3.08%</td>\n",
       "      <td id=\"T_4139f_row23_col3\" class=\"data row23 col3\" >-2.06%</td>\n",
       "      <td id=\"T_4139f_row23_col4\" class=\"data row23 col4\" >-2.92%</td>\n",
       "      <td id=\"T_4139f_row23_col5\" class=\"data row23 col5\" >-3.22%</td>\n",
       "      <td id=\"T_4139f_row23_col6\" class=\"data row23 col6\" >-4.68%</td>\n",
       "      <td id=\"T_4139f_row23_col7\" class=\"data row23 col7\" >-1.88%</td>\n",
       "      <td id=\"T_4139f_row23_col8\" class=\"data row23 col8\" >-1.98%</td>\n",
       "      <td id=\"T_4139f_row23_col9\" class=\"data row23 col9\" >-2.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4139f_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "      <td id=\"T_4139f_row24_col0\" class=\"data row24 col0\" >-0.20%</td>\n",
       "      <td id=\"T_4139f_row24_col1\" class=\"data row24 col1\" >-0.03%</td>\n",
       "      <td id=\"T_4139f_row24_col2\" class=\"data row24 col2\" >-0.31%</td>\n",
       "      <td id=\"T_4139f_row24_col3\" class=\"data row24 col3\" >0.07%</td>\n",
       "      <td id=\"T_4139f_row24_col4\" class=\"data row24 col4\" >-0.29%</td>\n",
       "      <td id=\"T_4139f_row24_col5\" class=\"data row24 col5\" >-0.38%</td>\n",
       "      <td id=\"T_4139f_row24_col6\" class=\"data row24 col6\" >-0.80%</td>\n",
       "      <td id=\"T_4139f_row24_col7\" class=\"data row24 col7\" >-0.94%</td>\n",
       "      <td id=\"T_4139f_row24_col8\" class=\"data row24 col8\" >-0.66%</td>\n",
       "      <td id=\"T_4139f_row24_col9\" class=\"data row24 col9\" >-1.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x169123f50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Estimating Risk Parity Portfolios with PCR Regression\n",
    "\n",
    "### 4.1 Estimating the Loadings Matrix with PCR\n",
    "\n",
    "This part is just to visualize how Riskfolio-Lib calculates a loadings matrix using PCR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "}\n",
       "#T_b391b_row7_col0 {\n",
       "  background-color: #097940;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row7_col2 {\n",
       "  background-color: #3ca959;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row7_col3, #T_b391b_row15_col0 {\n",
       "  background-color: #f7fcb4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row7_col4, #T_b391b_row23_col1 {\n",
       "  background-color: #fdad60;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row7_col5 {\n",
       "  background-color: #b1de71;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row8_col0 {\n",
       "  background-color: #9dd569;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row8_col1, #T_b391b_row18_col2 {\n",
       "  background-color: #f8fcb6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row8_col2 {\n",
       "  background-color: #07753e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row8_col3 {\n",
       "  background-color: #fffab6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row8_col4 {\n",
       "  background-color: #fa9656;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row8_col5 {\n",
       "  background-color: #96d268;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row9_col0 {\n",
       "  background-color: #e6f59d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row9_col1, #T_b391b_row11_col4 {\n",
       "  background-color: #fece7c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row9_col2 {\n",
       "  background-color: #c3e67d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row9_col3, #T_b391b_row19_col3 {\n",
       "  background-color: #e5f49b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row9_col4 {\n",
       "  background-color: #fee28f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row9_col5 {\n",
       "  background-color: #e9f6a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row10_col2, #T_b391b_row17_col4 {\n",
       "  background-color: #15904c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row10_col3, #T_b391b_row12_col2 {\n",
       "  background-color: #fffcba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row10_col4 {\n",
       "  background-color: #e54e35;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row10_col5 {\n",
       "  background-color: #6ec064;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row11_col1, #T_b391b_row12_col5 {\n",
       "  background-color: #fed07e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row11_col2 {\n",
       "  background-color: #9bd469;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row11_col3 {\n",
       "  background-color: #dff293;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row11_col5, #T_b391b_row21_col1 {\n",
       "  background-color: #d5ed88;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row12_col0 {\n",
       "  background-color: #66bd63;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row12_col3 {\n",
       "  background-color: #f4fab0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row12_col4 {\n",
       "  background-color: #d3ec87;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row13_col0 {\n",
       "  background-color: #f57547;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row13_col1, #T_b391b_row17_col1, #T_b391b_row19_col1 {\n",
       "  background-color: #fdbb6c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row13_col3 {\n",
       "  background-color: #ddf191;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row13_col4, #T_b391b_row22_col5 {\n",
       "  background-color: #87cb67;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row13_col5 {\n",
       "  background-color: #f67f4b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row14_col5 {\n",
       "  background-color: #fff6b0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row15_col1 {\n",
       "  background-color: #fba35c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row15_col3 {\n",
       "  background-color: #c5e67e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row16_col0 {\n",
       "  background-color: #7fc866;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row16_col1 {\n",
       "  background-color: #fdb96a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row16_col2 {\n",
       "  background-color: #45ad5b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row16_col4, #T_b391b_row22_col4, #T_b391b_row23_col2 {\n",
       "  background-color: #f7844e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row16_col5 {\n",
       "  background-color: #89cc67;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row17_col0 {\n",
       "  background-color: #e44c34;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row17_col2 {\n",
       "  background-color: #c82227;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row17_col5 {\n",
       "  background-color: #c21c27;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row18_col0 {\n",
       "  background-color: #db382b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row18_col1 {\n",
       "  background-color: #fca85e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row18_col5 {\n",
       "  background-color: #fff2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row19_col2, #T_b391b_row21_col5 {\n",
       "  background-color: #fee695;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row19_col4 {\n",
       "  background-color: #ecf7a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row19_col5 {\n",
       "  background-color: #fee08b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row20_col0 {\n",
       "  background-color: #128a49;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row20_col1 {\n",
       "  background-color: #feea9b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row20_col3 {\n",
       "  background-color: #fff5ae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row20_col4, #T_b391b_row21_col3 {\n",
       "  background-color: #fede89;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row21_col0 {\n",
       "  background-color: #8ccd67;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row21_col2 {\n",
       "  background-color: #a0d669;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row21_col4 {\n",
       "  background-color: #e0f295;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row22_col0 {\n",
       "  background-color: #f36b42;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row22_col1 {\n",
       "  background-color: #fed27f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row22_col2 {\n",
       "  background-color: #249d53;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row23_col0, #T_b391b_row23_col3 {\n",
       "  background-color: #d7ee8a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row23_col4 {\n",
       "  background-color: #8ecf67;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row23_col5 {\n",
       "  background-color: #f7814c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row24_col0 {\n",
       "  background-color: #0b7d42;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row24_col1 {\n",
       "  background-color: #fa9857;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row24_col2 {\n",
       "  background-color: #04703b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b391b_row24_col3 {\n",
       "  background-color: #bbe278;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b391b_row24_col5 {\n",
       "  background-color: #0e8245;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b391b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b391b_level0_col0\" class=\"col_heading level0 col0\" >const</th>\n",
       "      <th id=\"T_b391b_level0_col1\" class=\"col_heading level0 col1\" >MTUM</th>\n",
       "      <th id=\"T_b391b_level0_col2\" class=\"col_heading level0 col2\" >QUAL</th>\n",
       "      <th id=\"T_b391b_level0_col3\" class=\"col_heading level0 col3\" >SIZE</th>\n",
       "      <th id=\"T_b391b_level0_col4\" class=\"col_heading level0 col4\" >USMV</th>\n",
       "      <th id=\"T_b391b_level0_col5\" class=\"col_heading level0 col5\" >VLUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "      <td id=\"T_b391b_row0_col0\" class=\"data row0 col0\" >-0.0006</td>\n",
       "      <td id=\"T_b391b_row0_col1\" class=\"data row0 col1\" >-0.6174</td>\n",
       "      <td id=\"T_b391b_row0_col2\" class=\"data row0 col2\" >0.3847</td>\n",
       "      <td id=\"T_b391b_row0_col3\" class=\"data row0 col3\" >1.0857</td>\n",
       "      <td id=\"T_b391b_row0_col4\" class=\"data row0 col4\" >-1.1434</td>\n",
       "      <td id=\"T_b391b_row0_col5\" class=\"data row0 col5\" >1.4537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "      <td id=\"T_b391b_row1_col0\" class=\"data row1 col0\" >0.0005</td>\n",
       "      <td id=\"T_b391b_row1_col1\" class=\"data row1 col1\" >0.2236</td>\n",
       "      <td id=\"T_b391b_row1_col2\" class=\"data row1 col2\" >0.3103</td>\n",
       "      <td id=\"T_b391b_row1_col3\" class=\"data row1 col3\" >0.2055</td>\n",
       "      <td id=\"T_b391b_row1_col4\" class=\"data row1 col4\" >-0.0523</td>\n",
       "      <td id=\"T_b391b_row1_col5\" class=\"data row1 col5\" >0.4583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "      <td id=\"T_b391b_row2_col0\" class=\"data row2 col0\" >0.0003</td>\n",
       "      <td id=\"T_b391b_row2_col1\" class=\"data row2 col1\" >0.3086</td>\n",
       "      <td id=\"T_b391b_row2_col2\" class=\"data row2 col2\" >0.1875</td>\n",
       "      <td id=\"T_b391b_row2_col3\" class=\"data row2 col3\" >0.0764</td>\n",
       "      <td id=\"T_b391b_row2_col4\" class=\"data row2 col4\" >0.5222</td>\n",
       "      <td id=\"T_b391b_row2_col5\" class=\"data row2 col5\" >-0.0462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "      <td id=\"T_b391b_row3_col0\" class=\"data row3 col0\" >-0.0003</td>\n",
       "      <td id=\"T_b391b_row3_col1\" class=\"data row3 col1\" >0.0716</td>\n",
       "      <td id=\"T_b391b_row3_col2\" class=\"data row3 col2\" >0.1550</td>\n",
       "      <td id=\"T_b391b_row3_col3\" class=\"data row3 col3\" >0.2640</td>\n",
       "      <td id=\"T_b391b_row3_col4\" class=\"data row3 col4\" >0.3029</td>\n",
       "      <td id=\"T_b391b_row3_col5\" class=\"data row3 col5\" >0.1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "      <td id=\"T_b391b_row4_col0\" class=\"data row4 col0\" >0.0001</td>\n",
       "      <td id=\"T_b391b_row4_col1\" class=\"data row4 col1\" >0.0825</td>\n",
       "      <td id=\"T_b391b_row4_col2\" class=\"data row4 col2\" >0.1750</td>\n",
       "      <td id=\"T_b391b_row4_col3\" class=\"data row4 col3\" >0.2755</td>\n",
       "      <td id=\"T_b391b_row4_col4\" class=\"data row4 col4\" >0.2803</td>\n",
       "      <td id=\"T_b391b_row4_col5\" class=\"data row4 col5\" >0.1424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "      <td id=\"T_b391b_row5_col0\" class=\"data row5 col0\" >0.0001</td>\n",
       "      <td id=\"T_b391b_row5_col1\" class=\"data row5 col1\" >-0.2225</td>\n",
       "      <td id=\"T_b391b_row5_col2\" class=\"data row5 col2\" >-0.0563</td>\n",
       "      <td id=\"T_b391b_row5_col3\" class=\"data row5 col3\" >0.5690</td>\n",
       "      <td id=\"T_b391b_row5_col4\" class=\"data row5 col4\" >1.1192</td>\n",
       "      <td id=\"T_b391b_row5_col5\" class=\"data row5 col5\" >-0.4869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "      <td id=\"T_b391b_row6_col0\" class=\"data row6 col0\" >-0.0003</td>\n",
       "      <td id=\"T_b391b_row6_col1\" class=\"data row6 col1\" >-0.2158</td>\n",
       "      <td id=\"T_b391b_row6_col2\" class=\"data row6 col2\" >-0.0890</td>\n",
       "      <td id=\"T_b391b_row6_col3\" class=\"data row6 col3\" >0.5280</td>\n",
       "      <td id=\"T_b391b_row6_col4\" class=\"data row6 col4\" >1.1880</td>\n",
       "      <td id=\"T_b391b_row6_col5\" class=\"data row6 col5\" >-0.5770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "      <td id=\"T_b391b_row7_col0\" class=\"data row7 col0\" >0.0005</td>\n",
       "      <td id=\"T_b391b_row7_col1\" class=\"data row7 col1\" >0.0756</td>\n",
       "      <td id=\"T_b391b_row7_col2\" class=\"data row7 col2\" >0.3323</td>\n",
       "      <td id=\"T_b391b_row7_col3\" class=\"data row7 col3\" >0.3341</td>\n",
       "      <td id=\"T_b391b_row7_col4\" class=\"data row7 col4\" >-0.3697</td>\n",
       "      <td id=\"T_b391b_row7_col5\" class=\"data row7 col5\" >0.7041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "      <td id=\"T_b391b_row8_col0\" class=\"data row8 col0\" >0.0002</td>\n",
       "      <td id=\"T_b391b_row8_col1\" class=\"data row8 col1\" >0.1799</td>\n",
       "      <td id=\"T_b391b_row8_col2\" class=\"data row8 col2\" >0.3985</td>\n",
       "      <td id=\"T_b391b_row8_col3\" class=\"data row8 col3\" >0.2753</td>\n",
       "      <td id=\"T_b391b_row8_col4\" class=\"data row8 col4\" >-0.4646</td>\n",
       "      <td id=\"T_b391b_row8_col5\" class=\"data row8 col5\" >0.8125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "      <td id=\"T_b391b_row9_col0\" class=\"data row9 col0\" >0.0000</td>\n",
       "      <td id=\"T_b391b_row9_col1\" class=\"data row9 col1\" >-0.0535</td>\n",
       "      <td id=\"T_b391b_row9_col2\" class=\"data row9 col2\" >0.2147</td>\n",
       "      <td id=\"T_b391b_row9_col3\" class=\"data row9 col3\" >0.4089</td>\n",
       "      <td id=\"T_b391b_row9_col4\" class=\"data row9 col4\" >-0.0640</td>\n",
       "      <td id=\"T_b391b_row9_col5\" class=\"data row9 col5\" >0.4251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "      <td id=\"T_b391b_row10_col0\" class=\"data row10 col0\" >0.0005</td>\n",
       "      <td id=\"T_b391b_row10_col1\" class=\"data row10 col1\" >0.0537</td>\n",
       "      <td id=\"T_b391b_row10_col2\" class=\"data row10 col2\" >0.3676</td>\n",
       "      <td id=\"T_b391b_row10_col3\" class=\"data row10 col3\" >0.2851</td>\n",
       "      <td id=\"T_b391b_row10_col4\" class=\"data row10 col4\" >-0.7861</td>\n",
       "      <td id=\"T_b391b_row10_col5\" class=\"data row10 col5\" >0.9582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "      <td id=\"T_b391b_row11_col0\" class=\"data row11 col0\" >-0.0001</td>\n",
       "      <td id=\"T_b391b_row11_col1\" class=\"data row11 col1\" >-0.0477</td>\n",
       "      <td id=\"T_b391b_row11_col2\" class=\"data row11 col2\" >0.2557</td>\n",
       "      <td id=\"T_b391b_row11_col3\" class=\"data row11 col3\" >0.4299</td>\n",
       "      <td id=\"T_b391b_row11_col4\" class=\"data row11 col4\" >-0.1855</td>\n",
       "      <td id=\"T_b391b_row11_col5\" class=\"data row11 col5\" >0.5469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "      <td id=\"T_b391b_row12_col0\" class=\"data row12 col0\" >0.0003</td>\n",
       "      <td id=\"T_b391b_row12_col1\" class=\"data row12 col1\" >0.0234</td>\n",
       "      <td id=\"T_b391b_row12_col2\" class=\"data row12 col2\" >0.1293</td>\n",
       "      <td id=\"T_b391b_row12_col3\" class=\"data row12 col3\" >0.3482</td>\n",
       "      <td id=\"T_b391b_row12_col4\" class=\"data row12 col4\" >0.4953</td>\n",
       "      <td id=\"T_b391b_row12_col5\" class=\"data row12 col5\" >-0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "      <td id=\"T_b391b_row13_col0\" class=\"data row13 col0\" >-0.0003</td>\n",
       "      <td id=\"T_b391b_row13_col1\" class=\"data row13 col1\" >-0.1141</td>\n",
       "      <td id=\"T_b391b_row13_col2\" class=\"data row13 col2\" >-0.0021</td>\n",
       "      <td id=\"T_b391b_row13_col3\" class=\"data row13 col3\" >0.4376</td>\n",
       "      <td id=\"T_b391b_row13_col4\" class=\"data row13 col4\" >0.8811</td>\n",
       "      <td id=\"T_b391b_row13_col5\" class=\"data row13 col5\" >-0.3382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "      <td id=\"T_b391b_row14_col0\" class=\"data row14 col0\" >0.0005</td>\n",
       "      <td id=\"T_b391b_row14_col1\" class=\"data row14 col1\" >0.9280</td>\n",
       "      <td id=\"T_b391b_row14_col2\" class=\"data row14 col2\" >0.4153</td>\n",
       "      <td id=\"T_b391b_row14_col3\" class=\"data row14 col3\" >-0.4857</td>\n",
       "      <td id=\"T_b391b_row14_col4\" class=\"data row14 col4\" >0.1509</td>\n",
       "      <td id=\"T_b391b_row14_col5\" class=\"data row14 col5\" >0.2270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "      <td id=\"T_b391b_row15_col0\" class=\"data row15 col0\" >0.0000</td>\n",
       "      <td id=\"T_b391b_row15_col1\" class=\"data row15 col1\" >-0.1805</td>\n",
       "      <td id=\"T_b391b_row15_col2\" class=\"data row15 col2\" >-0.1459</td>\n",
       "      <td id=\"T_b391b_row15_col3\" class=\"data row15 col3\" >0.5152</td>\n",
       "      <td id=\"T_b391b_row15_col4\" class=\"data row15 col4\" >1.5802</td>\n",
       "      <td id=\"T_b391b_row15_col5\" class=\"data row15 col5\" >-0.8640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "      <td id=\"T_b391b_row16_col0\" class=\"data row16 col0\" >0.0003</td>\n",
       "      <td id=\"T_b391b_row16_col1\" class=\"data row16 col1\" >-0.1179</td>\n",
       "      <td id=\"T_b391b_row16_col2\" class=\"data row16 col2\" >0.3275</td>\n",
       "      <td id=\"T_b391b_row16_col3\" class=\"data row16 col3\" >0.5273</td>\n",
       "      <td id=\"T_b391b_row16_col4\" class=\"data row16 col4\" >-0.5373</td>\n",
       "      <td id=\"T_b391b_row16_col5\" class=\"data row16 col5\" >0.8583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "      <td id=\"T_b391b_row17_col0\" class=\"data row17 col0\" >-0.0004</td>\n",
       "      <td id=\"T_b391b_row17_col1\" class=\"data row17 col1\" >-0.1124</td>\n",
       "      <td id=\"T_b391b_row17_col2\" class=\"data row17 col2\" >-0.1054</td>\n",
       "      <td id=\"T_b391b_row17_col3\" class=\"data row17 col3\" >0.4160</td>\n",
       "      <td id=\"T_b391b_row17_col4\" class=\"data row17 col4\" >1.3474</td>\n",
       "      <td id=\"T_b391b_row17_col5\" class=\"data row17 col5\" >-0.7230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_b391b_row18_col0\" class=\"data row18 col0\" >-0.0004</td>\n",
       "      <td id=\"T_b391b_row18_col1\" class=\"data row18 col1\" >-0.1698</td>\n",
       "      <td id=\"T_b391b_row18_col2\" class=\"data row18 col2\" >0.1456</td>\n",
       "      <td id=\"T_b391b_row18_col3\" class=\"data row18 col3\" >0.5654</td>\n",
       "      <td id=\"T_b391b_row18_col4\" class=\"data row18 col4\" >0.2944</td>\n",
       "      <td id=\"T_b391b_row18_col5\" class=\"data row18 col5\" >0.2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "      <td id=\"T_b391b_row19_col0\" class=\"data row19 col0\" >0.0001</td>\n",
       "      <td id=\"T_b391b_row19_col1\" class=\"data row19 col1\" >-0.1114</td>\n",
       "      <td id=\"T_b391b_row19_col2\" class=\"data row19 col2\" >0.0902</td>\n",
       "      <td id=\"T_b391b_row19_col3\" class=\"data row19 col3\" >0.4059</td>\n",
       "      <td id=\"T_b391b_row19_col4\" class=\"data row19 col4\" >0.3260</td>\n",
       "      <td id=\"T_b391b_row19_col5\" class=\"data row19 col5\" >0.0646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "      <td id=\"T_b391b_row20_col0\" class=\"data row20 col0\" >0.0004</td>\n",
       "      <td id=\"T_b391b_row20_col1\" class=\"data row20 col1\" >0.0523</td>\n",
       "      <td id=\"T_b391b_row20_col2\" class=\"data row20 col2\" >0.2066</td>\n",
       "      <td id=\"T_b391b_row20_col3\" class=\"data row20 col3\" >0.2454</td>\n",
       "      <td id=\"T_b391b_row20_col4\" class=\"data row20 col4\" >-0.0941</td>\n",
       "      <td id=\"T_b391b_row20_col5\" class=\"data row20 col5\" >0.3757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "      <td id=\"T_b391b_row21_col0\" class=\"data row21 col0\" >0.0002</td>\n",
       "      <td id=\"T_b391b_row21_col1\" class=\"data row21 col1\" >0.3213</td>\n",
       "      <td id=\"T_b391b_row21_col2\" class=\"data row21 col2\" >0.2528</td>\n",
       "      <td id=\"T_b391b_row21_col3\" class=\"data row21 col3\" >0.1344</td>\n",
       "      <td id=\"T_b391b_row21_col4\" class=\"data row21 col4\" >0.4175</td>\n",
       "      <td id=\"T_b391b_row21_col5\" class=\"data row21 col5\" >0.1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "      <td id=\"T_b391b_row22_col0\" class=\"data row22 col0\" >-0.0004</td>\n",
       "      <td id=\"T_b391b_row22_col1\" class=\"data row22 col1\" >-0.0401</td>\n",
       "      <td id=\"T_b391b_row22_col2\" class=\"data row22 col2\" >0.3498</td>\n",
       "      <td id=\"T_b391b_row22_col3\" class=\"data row22 col3\" >0.4581</td>\n",
       "      <td id=\"T_b391b_row22_col4\" class=\"data row22 col4\" >-0.5477</td>\n",
       "      <td id=\"T_b391b_row22_col5\" class=\"data row22 col5\" >0.8658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "      <td id=\"T_b391b_row23_col0\" class=\"data row23 col0\" >0.0001</td>\n",
       "      <td id=\"T_b391b_row23_col1\" class=\"data row23 col1\" >-0.1570</td>\n",
       "      <td id=\"T_b391b_row23_col2\" class=\"data row23 col2\" >-0.0138</td>\n",
       "      <td id=\"T_b391b_row23_col3\" class=\"data row23 col3\" >0.4624</td>\n",
       "      <td id=\"T_b391b_row23_col4\" class=\"data row23 col4\" >0.8432</td>\n",
       "      <td id=\"T_b391b_row23_col5\" class=\"data row23 col5\" >-0.3221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b391b_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "      <td id=\"T_b391b_row24_col0\" class=\"data row24 col0\" >0.0005</td>\n",
       "      <td id=\"T_b391b_row24_col1\" class=\"data row24 col1\" >-0.2022</td>\n",
       "      <td id=\"T_b391b_row24_col2\" class=\"data row24 col2\" >0.4050</td>\n",
       "      <td id=\"T_b391b_row24_col3\" class=\"data row24 col3\" >0.5509</td>\n",
       "      <td id=\"T_b391b_row24_col4\" class=\"data row24 col4\" >-1.1994</td>\n",
       "      <td id=\"T_b391b_row24_col5\" class=\"data row24 col5\" >1.3225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13e06e290>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_selection = 'PCR' # Method to select best model, could be PCR or Stepwise\n",
    "n_components = 0.95 # 95% of explained variance. See PCA in scikit learn for more information\n",
    "\n",
    "loadings = rp.loadings_matrix(X=X,\n",
    "                              Y=Y,\n",
    "                              feature_selection=feature_selection,\n",
    "                              n_components=n_components)\n",
    "\n",
    "loadings.style.format(\"{:.4f}\").background_gradient(cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calculating the Factor Risk Parity Portfolio based on Principal Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>21.3582%</td>\n",
       "      <td>-2.2193%</td>\n",
       "      <td>-4.4723%</td>\n",
       "      <td>1.6638%</td>\n",
       "      <td>1.5454%</td>\n",
       "      <td>11.2643%</td>\n",
       "      <td>10.8018%</td>\n",
       "      <td>1.5183%</td>\n",
       "      <td>-1.1621%</td>\n",
       "      <td>5.0755%</td>\n",
       "      <td>...</td>\n",
       "      <td>10.4574%</td>\n",
       "      <td>6.9456%</td>\n",
       "      <td>7.8930%</td>\n",
       "      <td>9.2757%</td>\n",
       "      <td>6.5919%</td>\n",
       "      <td>1.3817%</td>\n",
       "      <td>-4.2466%</td>\n",
       "      <td>4.7591%</td>\n",
       "      <td>8.6248%</td>\n",
       "      <td>8.0172%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             APA       BA      BAX     BMY   CMCSA      CNP      CPB      DE  \\\n",
       "weights 21.3582% -2.2193% -4.4723% 1.6638% 1.5454% 11.2643% 10.8018% 1.5183%   \n",
       "\n",
       "             HPQ     JCI  ...       NI    PCAR     PSA     SEE       T  \\\n",
       "weights -1.1621% 5.0755%  ... 10.4574% 6.9456% 7.8930% 9.2757% 6.5919%   \n",
       "\n",
       "            TGT      TMO     TXT      VZ    ZION  \n",
       "weights 1.3817% -4.2466% 4.7591% 8.6248% 8.0172%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_selection = 'PCR' # Method to select best model, could be PCR or Stepwise\n",
    "n_components = 0.95 # 95% of explained variance. See PCA in scikit learn for more information\n",
    "\n",
    "port.factors = X\n",
    "port.factors_stats(method_mu=method_mu,\n",
    "                   method_cov=method_cov,\n",
    "                   feature_selection=feature_selection,\n",
    "                   n_components=n_components\n",
    "                  )\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model = 'FC' # Factor Contribution Model\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "rf = 0 # Risk free rate\n",
    "b_f = None # Risk factor contribution vector\n",
    "\n",
    "w = port.rp_optimization(model=model,\n",
    "                         rm=rm,\n",
    "                         rf=rf,\n",
    "                         b_f=b_f,\n",
    "                         )\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the composition of the portfolio\n",
    "ax = rp.plot_bar(w,\n",
    "                 title='Factor Risk Parity Portfolio - Variance',\n",
    "                 kind=\"v\",\n",
    "                 others=0.05,\n",
    "                 nrow=25,\n",
    "                 height=6,\n",
    "                 width=10,\n",
    "                 ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Calculate the risk contribution per asset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rp.plot_risk_con(w,\n",
    "                      cov=port.cov,\n",
    "                      returns=port.returns,\n",
    "                      rm=rm,\n",
    "                      rf=0,\n",
    "                      alpha=0.05,\n",
    "                      color=\"tab:blue\",\n",
    "                      height=6,\n",
    "                      width=10,\n",
    "                      ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Calculate the risk contribution per principal component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = rp.plot_factor_risk_con(\n",
    "    w,\n",
    "    cov=port.cov,\n",
    "    returns=port.returns,\n",
    "    factors=port.factors,\n",
    "    rm=rm,\n",
    "    rf=0,\n",
    "    feature_selection=feature_selection,\n",
    "    stepwise=stepwise,\n",
    "    percentage=True,\n",
    "    erc_line=True,\n",
    "    color=\"tab:orange\",\n",
    "    ax=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Estimating Portfolios Using Risk Factors with Other Risk Measures and PCR\n",
    "\n",
    "In this part I will calculate optimal factor risk parity portfolios for several risk measures using the principal components of market factors matrix as risk factors.\n",
    "\n",
    "### 5.1 Calculate Optimal Factor Risk Parity Portfolios based on Principal Components for Several Risk Measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Standard Deviation.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk.\n",
    "# 'WR': Worst Realization (Minimax)\n",
    "# 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).\n",
    "# 'ADD': Average Drawdown of uncompounded cumulative returns.\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM', 'CVaR',\n",
    "       'EVaR', 'CDaR', 'UCI', 'EDaR']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "# When we use hist = True the risk measures all calculated\n",
    "# using historical returns, while when hist = False the\n",
    "# risk measures are calculated using the expected returns \n",
    "#  based on risk factor model: R = a + B * F\n",
    "\n",
    "for i in rms:\n",
    "    w = port.rp_optimization(model=model, rm=i, rf=rf, b_f=b_f)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row1_col3, #T_baf03_row1_col9, #T_baf03_row10_col7, #T_baf03_row21_col8 {\n",
       "  background-color: #88cd7f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row1_col7 {\n",
       "  background-color: #83cb7d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row1_col8, #T_baf03_row21_col7 {\n",
       "  background-color: #84cb7e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row2_col0, #T_baf03_row2_col1, #T_baf03_row2_col2, #T_baf03_row2_col4, #T_baf03_row21_col5, #T_baf03_row21_col6 {\n",
       "  background-color: #9fd788;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row2_col3, #T_baf03_row21_col0, #T_baf03_row21_col2, #T_baf03_row21_col4 {\n",
       "  background-color: #9dd688;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row2_col5, #T_baf03_row2_col6 {\n",
       "  background-color: #a1d889;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row2_col7 {\n",
       "  background-color: #92d183;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row2_col8 {\n",
       "  background-color: #98d486;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row2_col9, #T_baf03_row21_col1 {\n",
       "  background-color: #9cd687;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row3_col0, #T_baf03_row3_col1, #T_baf03_row3_col2, #T_baf03_row3_col3, #T_baf03_row3_col4, #T_baf03_row3_col5, #T_baf03_row3_col6, #T_baf03_row4_col3 {\n",
       "  background-color: #62bb6e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row3_col7 {\n",
       "  background-color: #61bb6d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row3_col8, #T_baf03_row7_col7, #T_baf03_row7_col9, #T_baf03_row10_col2, #T_baf03_row10_col5, #T_baf03_row20_col3 {\n",
       "  background-color: #68be71;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row3_col9, #T_baf03_row7_col8, #T_baf03_row10_col0, #T_baf03_row10_col1, #T_baf03_row10_col4 {\n",
       "  background-color: #69bf72;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row4_col0, #T_baf03_row4_col1, #T_baf03_row4_col2, #T_baf03_row4_col4, #T_baf03_row4_col5, #T_baf03_row4_col6, #T_baf03_row4_col8, #T_baf03_row7_col0, #T_baf03_row7_col1, #T_baf03_row7_col2, #T_baf03_row7_col3, #T_baf03_row7_col4, #T_baf03_row7_col5, #T_baf03_row7_col6, #T_baf03_row20_col2, #T_baf03_row20_col4, #T_baf03_row20_col5, #T_baf03_row20_col6 {\n",
       "  background-color: #64bc6f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row4_col7 {\n",
       "  background-color: #5fba6c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row4_col9, #T_baf03_row10_col6, #T_baf03_row20_col0, #T_baf03_row20_col1 {\n",
       "  background-color: #66bd70;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row5_col0, #T_baf03_row5_col4, #T_baf03_row18_col7 {\n",
       "  background-color: #1e8041;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row5_col1, #T_baf03_row6_col9 {\n",
       "  background-color: #1d7f41;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row5_col2, #T_baf03_row5_col5, #T_baf03_row6_col3, #T_baf03_row17_col7 {\n",
       "  background-color: #1f8142;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row5_col3 {\n",
       "  background-color: #1c7e40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row5_col6, #T_baf03_row6_col0, #T_baf03_row6_col1, #T_baf03_row15_col3 {\n",
       "  background-color: #208242;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row5_col7 {\n",
       "  background-color: #076d39;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row5_col8, #T_baf03_row15_col8 {\n",
       "  background-color: #0e743c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row5_col9, #T_baf03_row6_col8 {\n",
       "  background-color: #167a3f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row6_col2, #T_baf03_row6_col4 {\n",
       "  background-color: #218242;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row6_col5, #T_baf03_row6_col6, #T_baf03_row15_col0, #T_baf03_row15_col1, #T_baf03_row18_col8 {\n",
       "  background-color: #228343;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row6_col7 {\n",
       "  background-color: #0d733c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row8_col0, #T_baf03_row8_col1, #T_baf03_row8_col2, #T_baf03_row8_col3, #T_baf03_row8_col4, #T_baf03_row8_col7, #T_baf03_row8_col8, #T_baf03_row8_col9, #T_baf03_row20_col8 {\n",
       "  background-color: #7ec97b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row8_col5, #T_baf03_row8_col6, #T_baf03_row10_col9 {\n",
       "  background-color: #7fc97c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row9_col0, #T_baf03_row9_col1, #T_baf03_row9_col2, #T_baf03_row9_col3, #T_baf03_row9_col4, #T_baf03_row9_col5, #T_baf03_row9_col6, #T_baf03_row11_col0, #T_baf03_row11_col1, #T_baf03_row11_col2, #T_baf03_row11_col3, #T_baf03_row11_col4, #T_baf03_row11_col5, #T_baf03_row11_col6 {\n",
       "  background-color: #40aa5c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row9_col7, #T_baf03_row22_col0, #T_baf03_row22_col1, #T_baf03_row22_col2, #T_baf03_row22_col3, #T_baf03_row22_col4, #T_baf03_row22_col5 {\n",
       "  background-color: #43ac5e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row9_col8, #T_baf03_row22_col7, #T_baf03_row22_col9 {\n",
       "  background-color: #49af61;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row9_col9, #T_baf03_row24_col7, #T_baf03_row24_col8 {\n",
       "  background-color: #47ae60;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row10_col3 {\n",
       "  background-color: #6dc073;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row11_col7, #T_baf03_row22_col6 {\n",
       "  background-color: #42ab5d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row11_col8, #T_baf03_row11_col9, #T_baf03_row12_col8 {\n",
       "  background-color: #45ad5f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row12_col0, #T_baf03_row12_col1, #T_baf03_row12_col4 {\n",
       "  background-color: #4eb163;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row12_col2, #T_baf03_row12_col5, #T_baf03_row12_col6 {\n",
       "  background-color: #4fb264;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row12_col3, #T_baf03_row12_col9 {\n",
       "  background-color: #4cb063;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row12_col7 {\n",
       "  background-color: #3fa95c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row13_col0, #T_baf03_row13_col1, #T_baf03_row13_col4, #T_baf03_row17_col5, #T_baf03_row17_col6, #T_baf03_row24_col3 {\n",
       "  background-color: #329850;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row13_col2, #T_baf03_row13_col5, #T_baf03_row13_col6 {\n",
       "  background-color: #339951;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row13_col3, #T_baf03_row13_col9, #T_baf03_row17_col2, #T_baf03_row17_col4 {\n",
       "  background-color: #31974f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row13_col7, #T_baf03_row17_col8 {\n",
       "  background-color: #278946;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row13_col8, #T_baf03_row17_col9, #T_baf03_row23_col2, #T_baf03_row23_col5, #T_baf03_row23_col6 {\n",
       "  background-color: #2e924c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row14_col0, #T_baf03_row14_col1, #T_baf03_row14_col2, #T_baf03_row14_col3, #T_baf03_row14_col4, #T_baf03_row14_col5, #T_baf03_row14_col6, #T_baf03_row14_col7, #T_baf03_row14_col8, #T_baf03_row14_col9 {\n",
       "  background-color: #ffffe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row15_col2 {\n",
       "  background-color: #248644;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row15_col4, #T_baf03_row23_col7 {\n",
       "  background-color: #238443;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row15_col5, #T_baf03_row15_col6 {\n",
       "  background-color: #258745;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row15_col7 {\n",
       "  background-color: #056c39;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row15_col9 {\n",
       "  background-color: #187b3f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row16_col0, #T_baf03_row16_col1, #T_baf03_row16_col2, #T_baf03_row16_col3, #T_baf03_row16_col4, #T_baf03_row16_col5, #T_baf03_row16_col6 {\n",
       "  background-color: #369d54;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row16_col7, #T_baf03_row16_col9, #T_baf03_row19_col7 {\n",
       "  background-color: #39a056;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row16_col8 {\n",
       "  background-color: #3aa257;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row17_col0, #T_baf03_row17_col1, #T_baf03_row24_col0, #T_baf03_row24_col1 {\n",
       "  background-color: #30954f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row17_col3, #T_baf03_row24_col2, #T_baf03_row24_col4 {\n",
       "  background-color: #2f944e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row18_col0, #T_baf03_row18_col1 {\n",
       "  background-color: #298c48;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row18_col2, #T_baf03_row18_col4, #T_baf03_row18_col5, #T_baf03_row18_col6, #T_baf03_row23_col8 {\n",
       "  background-color: #2a8d49;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row18_col3 {\n",
       "  background-color: #288a47;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row18_col9 {\n",
       "  background-color: #268846;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row19_col0, #T_baf03_row19_col1, #T_baf03_row19_col2, #T_baf03_row19_col3, #T_baf03_row19_col4, #T_baf03_row19_col5, #T_baf03_row19_col6 {\n",
       "  background-color: #389f55;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row19_col8 {\n",
       "  background-color: #3fa85b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row19_col9, #T_baf03_row24_col9 {\n",
       "  background-color: #3ea75a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row20_col7, #T_baf03_row20_col9 {\n",
       "  background-color: #77c679;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row21_col3 {\n",
       "  background-color: #9ad587;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row21_col9 {\n",
       "  background-color: #90d083;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_baf03_row22_col8 {\n",
       "  background-color: #4ab062;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row23_col0, #T_baf03_row23_col1, #T_baf03_row23_col4, #T_baf03_row23_col9 {\n",
       "  background-color: #2d914b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row23_col3 {\n",
       "  background-color: #2c8f4b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_baf03_row24_col5, #T_baf03_row24_col6 {\n",
       "  background-color: #2f934d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_baf03\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_baf03_level0_col0\" class=\"col_heading level0 col0\" >MV</th>\n",
       "      <th id=\"T_baf03_level0_col1\" class=\"col_heading level0 col1\" >MAD</th>\n",
       "      <th id=\"T_baf03_level0_col2\" class=\"col_heading level0 col2\" >MSV</th>\n",
       "      <th id=\"T_baf03_level0_col3\" class=\"col_heading level0 col3\" >FLPM</th>\n",
       "      <th id=\"T_baf03_level0_col4\" class=\"col_heading level0 col4\" >SLPM</th>\n",
       "      <th id=\"T_baf03_level0_col5\" class=\"col_heading level0 col5\" >CVaR</th>\n",
       "      <th id=\"T_baf03_level0_col6\" class=\"col_heading level0 col6\" >EVaR</th>\n",
       "      <th id=\"T_baf03_level0_col7\" class=\"col_heading level0 col7\" >CDaR</th>\n",
       "      <th id=\"T_baf03_level0_col8\" class=\"col_heading level0 col8\" >UCI</th>\n",
       "      <th id=\"T_baf03_level0_col9\" class=\"col_heading level0 col9\" >EDaR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "      <td id=\"T_baf03_row0_col0\" class=\"data row0 col0\" >21.36%</td>\n",
       "      <td id=\"T_baf03_row0_col1\" class=\"data row0 col1\" >21.13%</td>\n",
       "      <td id=\"T_baf03_row0_col2\" class=\"data row0 col2\" >23.67%</td>\n",
       "      <td id=\"T_baf03_row0_col3\" class=\"data row0 col3\" >18.19%</td>\n",
       "      <td id=\"T_baf03_row0_col4\" class=\"data row0 col4\" >22.16%</td>\n",
       "      <td id=\"T_baf03_row0_col5\" class=\"data row0 col5\" >24.30%</td>\n",
       "      <td id=\"T_baf03_row0_col6\" class=\"data row0 col6\" >25.75%</td>\n",
       "      <td id=\"T_baf03_row0_col7\" class=\"data row0 col7\" >9.46%</td>\n",
       "      <td id=\"T_baf03_row0_col8\" class=\"data row0 col8\" >9.25%</td>\n",
       "      <td id=\"T_baf03_row0_col9\" class=\"data row0 col9\" >10.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "      <td id=\"T_baf03_row1_col0\" class=\"data row1 col0\" >-2.22%</td>\n",
       "      <td id=\"T_baf03_row1_col1\" class=\"data row1 col1\" >-2.15%</td>\n",
       "      <td id=\"T_baf03_row1_col2\" class=\"data row1 col2\" >-3.03%</td>\n",
       "      <td id=\"T_baf03_row1_col3\" class=\"data row1 col3\" >-1.09%</td>\n",
       "      <td id=\"T_baf03_row1_col4\" class=\"data row1 col4\" >-2.51%</td>\n",
       "      <td id=\"T_baf03_row1_col5\" class=\"data row1 col5\" >-3.23%</td>\n",
       "      <td id=\"T_baf03_row1_col6\" class=\"data row1 col6\" >-3.77%</td>\n",
       "      <td id=\"T_baf03_row1_col7\" class=\"data row1 col7\" >1.97%</td>\n",
       "      <td id=\"T_baf03_row1_col8\" class=\"data row1 col8\" >2.23%</td>\n",
       "      <td id=\"T_baf03_row1_col9\" class=\"data row1 col9\" >1.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "      <td id=\"T_baf03_row2_col0\" class=\"data row2 col0\" >-4.47%</td>\n",
       "      <td id=\"T_baf03_row2_col1\" class=\"data row2 col1\" >-4.37%</td>\n",
       "      <td id=\"T_baf03_row2_col2\" class=\"data row2 col2\" >-5.62%</td>\n",
       "      <td id=\"T_baf03_row2_col3\" class=\"data row2 col3\" >-2.89%</td>\n",
       "      <td id=\"T_baf03_row2_col4\" class=\"data row2 col4\" >-4.88%</td>\n",
       "      <td id=\"T_baf03_row2_col5\" class=\"data row2 col5\" >-5.93%</td>\n",
       "      <td id=\"T_baf03_row2_col6\" class=\"data row2 col6\" >-6.66%</td>\n",
       "      <td id=\"T_baf03_row2_col7\" class=\"data row2 col7\" >1.45%</td>\n",
       "      <td id=\"T_baf03_row2_col8\" class=\"data row2 col8\" >1.64%</td>\n",
       "      <td id=\"T_baf03_row2_col9\" class=\"data row2 col9\" >0.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "      <td id=\"T_baf03_row3_col0\" class=\"data row3 col0\" >1.66%</td>\n",
       "      <td id=\"T_baf03_row3_col1\" class=\"data row3 col1\" >1.69%</td>\n",
       "      <td id=\"T_baf03_row3_col2\" class=\"data row3 col2\" >1.39%</td>\n",
       "      <td id=\"T_baf03_row3_col3\" class=\"data row3 col3\" >2.04%</td>\n",
       "      <td id=\"T_baf03_row3_col4\" class=\"data row3 col4\" >1.57%</td>\n",
       "      <td id=\"T_baf03_row3_col5\" class=\"data row3 col5\" >1.32%</td>\n",
       "      <td id=\"T_baf03_row3_col6\" class=\"data row3 col6\" >1.14%</td>\n",
       "      <td id=\"T_baf03_row3_col7\" class=\"data row3 col7\" >3.07%</td>\n",
       "      <td id=\"T_baf03_row3_col8\" class=\"data row3 col8\" >3.10%</td>\n",
       "      <td id=\"T_baf03_row3_col9\" class=\"data row3 col9\" >2.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "      <td id=\"T_baf03_row4_col0\" class=\"data row4 col0\" >1.55%</td>\n",
       "      <td id=\"T_baf03_row4_col1\" class=\"data row4 col1\" >1.57%</td>\n",
       "      <td id=\"T_baf03_row4_col2\" class=\"data row4 col2\" >1.23%</td>\n",
       "      <td id=\"T_baf03_row4_col3\" class=\"data row4 col3\" >1.98%</td>\n",
       "      <td id=\"T_baf03_row4_col4\" class=\"data row4 col4\" >1.44%</td>\n",
       "      <td id=\"T_baf03_row4_col5\" class=\"data row4 col5\" >1.15%</td>\n",
       "      <td id=\"T_baf03_row4_col6\" class=\"data row4 col6\" >0.95%</td>\n",
       "      <td id=\"T_baf03_row4_col7\" class=\"data row4 col7\" >3.16%</td>\n",
       "      <td id=\"T_baf03_row4_col8\" class=\"data row4 col8\" >3.20%</td>\n",
       "      <td id=\"T_baf03_row4_col9\" class=\"data row4 col9\" >2.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "      <td id=\"T_baf03_row5_col0\" class=\"data row5 col0\" >11.26%</td>\n",
       "      <td id=\"T_baf03_row5_col1\" class=\"data row5 col1\" >11.22%</td>\n",
       "      <td id=\"T_baf03_row5_col2\" class=\"data row5 col2\" >12.03%</td>\n",
       "      <td id=\"T_baf03_row5_col3\" class=\"data row5 col3\" >10.20%</td>\n",
       "      <td id=\"T_baf03_row5_col4\" class=\"data row5 col4\" >11.54%</td>\n",
       "      <td id=\"T_baf03_row5_col5\" class=\"data row5 col5\" >12.18%</td>\n",
       "      <td id=\"T_baf03_row5_col6\" class=\"data row5 col6\" >12.72%</td>\n",
       "      <td id=\"T_baf03_row5_col7\" class=\"data row5 col7\" >7.32%</td>\n",
       "      <td id=\"T_baf03_row5_col8\" class=\"data row5 col8\" >6.89%</td>\n",
       "      <td id=\"T_baf03_row5_col9\" class=\"data row5 col9\" >7.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "      <td id=\"T_baf03_row6_col0\" class=\"data row6 col0\" >10.80%</td>\n",
       "      <td id=\"T_baf03_row6_col1\" class=\"data row6 col1\" >10.76%</td>\n",
       "      <td id=\"T_baf03_row6_col2\" class=\"data row6 col2\" >11.54%</td>\n",
       "      <td id=\"T_baf03_row6_col3\" class=\"data row6 col3\" >9.77%</td>\n",
       "      <td id=\"T_baf03_row6_col4\" class=\"data row6 col4\" >11.07%</td>\n",
       "      <td id=\"T_baf03_row6_col5\" class=\"data row6 col5\" >11.68%</td>\n",
       "      <td id=\"T_baf03_row6_col6\" class=\"data row6 col6\" >12.21%</td>\n",
       "      <td id=\"T_baf03_row6_col7\" class=\"data row6 col7\" >7.00%</td>\n",
       "      <td id=\"T_baf03_row6_col8\" class=\"data row6 col8\" >6.56%</td>\n",
       "      <td id=\"T_baf03_row6_col9\" class=\"data row6 col9\" >7.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "      <td id=\"T_baf03_row7_col0\" class=\"data row7 col0\" >1.52%</td>\n",
       "      <td id=\"T_baf03_row7_col1\" class=\"data row7 col1\" >1.53%</td>\n",
       "      <td id=\"T_baf03_row7_col2\" class=\"data row7 col2\" >1.26%</td>\n",
       "      <td id=\"T_baf03_row7_col3\" class=\"data row7 col3\" >1.88%</td>\n",
       "      <td id=\"T_baf03_row7_col4\" class=\"data row7 col4\" >1.42%</td>\n",
       "      <td id=\"T_baf03_row7_col5\" class=\"data row7 col5\" >1.22%</td>\n",
       "      <td id=\"T_baf03_row7_col6\" class=\"data row7 col6\" >1.03%</td>\n",
       "      <td id=\"T_baf03_row7_col7\" class=\"data row7 col7\" >2.85%</td>\n",
       "      <td id=\"T_baf03_row7_col8\" class=\"data row7 col8\" >3.05%</td>\n",
       "      <td id=\"T_baf03_row7_col9\" class=\"data row7 col9\" >2.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "      <td id=\"T_baf03_row8_col0\" class=\"data row8 col0\" >-1.16%</td>\n",
       "      <td id=\"T_baf03_row8_col1\" class=\"data row8 col1\" >-1.12%</td>\n",
       "      <td id=\"T_baf03_row8_col2\" class=\"data row8 col2\" >-1.80%</td>\n",
       "      <td id=\"T_baf03_row8_col3\" class=\"data row8 col3\" >-0.28%</td>\n",
       "      <td id=\"T_baf03_row8_col4\" class=\"data row8 col4\" >-1.39%</td>\n",
       "      <td id=\"T_baf03_row8_col5\" class=\"data row8 col5\" >-1.93%</td>\n",
       "      <td id=\"T_baf03_row8_col6\" class=\"data row8 col6\" >-2.37%</td>\n",
       "      <td id=\"T_baf03_row8_col7\" class=\"data row8 col7\" >2.11%</td>\n",
       "      <td id=\"T_baf03_row8_col8\" class=\"data row8 col8\" >2.44%</td>\n",
       "      <td id=\"T_baf03_row8_col9\" class=\"data row8 col9\" >2.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "      <td id=\"T_baf03_row9_col0\" class=\"data row9 col0\" >5.08%</td>\n",
       "      <td id=\"T_baf03_row9_col1\" class=\"data row9 col1\" >5.05%</td>\n",
       "      <td id=\"T_baf03_row9_col2\" class=\"data row9 col2\" >5.28%</td>\n",
       "      <td id=\"T_baf03_row9_col3\" class=\"data row9 col3\" >4.80%</td>\n",
       "      <td id=\"T_baf03_row9_col4\" class=\"data row9 col4\" >5.14%</td>\n",
       "      <td id=\"T_baf03_row9_col5\" class=\"data row9 col5\" >5.34%</td>\n",
       "      <td id=\"T_baf03_row9_col6\" class=\"data row9 col6\" >5.46%</td>\n",
       "      <td id=\"T_baf03_row9_col7\" class=\"data row9 col7\" >4.05%</td>\n",
       "      <td id=\"T_baf03_row9_col8\" class=\"data row9 col8\" >4.06%</td>\n",
       "      <td id=\"T_baf03_row9_col9\" class=\"data row9 col9\" >4.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "      <td id=\"T_baf03_row10_col0\" class=\"data row10 col0\" >0.96%</td>\n",
       "      <td id=\"T_baf03_row10_col1\" class=\"data row10 col1\" >0.95%</td>\n",
       "      <td id=\"T_baf03_row10_col2\" class=\"data row10 col2\" >0.80%</td>\n",
       "      <td id=\"T_baf03_row10_col3\" class=\"data row10 col3\" >1.19%</td>\n",
       "      <td id=\"T_baf03_row10_col4\" class=\"data row10 col4\" >0.89%</td>\n",
       "      <td id=\"T_baf03_row10_col5\" class=\"data row10 col5\" >0.80%</td>\n",
       "      <td id=\"T_baf03_row10_col6\" class=\"data row10 col6\" >0.65%</td>\n",
       "      <td id=\"T_baf03_row10_col7\" class=\"data row10 col7\" >1.78%</td>\n",
       "      <td id=\"T_baf03_row10_col8\" class=\"data row10 col8\" >2.06%</td>\n",
       "      <td id=\"T_baf03_row10_col9\" class=\"data row10 col9\" >2.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "      <td id=\"T_baf03_row11_col0\" class=\"data row11 col0\" >5.04%</td>\n",
       "      <td id=\"T_baf03_row11_col1\" class=\"data row11 col1\" >5.01%</td>\n",
       "      <td id=\"T_baf03_row11_col2\" class=\"data row11 col2\" >5.22%</td>\n",
       "      <td id=\"T_baf03_row11_col3\" class=\"data row11 col3\" >4.79%</td>\n",
       "      <td id=\"T_baf03_row11_col4\" class=\"data row11 col4\" >5.10%</td>\n",
       "      <td id=\"T_baf03_row11_col5\" class=\"data row11 col5\" >5.28%</td>\n",
       "      <td id=\"T_baf03_row11_col6\" class=\"data row11 col6\" >5.39%</td>\n",
       "      <td id=\"T_baf03_row11_col7\" class=\"data row11 col7\" >4.09%</td>\n",
       "      <td id=\"T_baf03_row11_col8\" class=\"data row11 col8\" >4.14%</td>\n",
       "      <td id=\"T_baf03_row11_col9\" class=\"data row11 col9\" >4.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "      <td id=\"T_baf03_row12_col0\" class=\"data row12 col0\" >3.65%</td>\n",
       "      <td id=\"T_baf03_row12_col1\" class=\"data row12 col1\" >3.67%</td>\n",
       "      <td id=\"T_baf03_row12_col2\" class=\"data row12 col2\" >3.54%</td>\n",
       "      <td id=\"T_baf03_row12_col3\" class=\"data row12 col3\" >3.79%</td>\n",
       "      <td id=\"T_baf03_row12_col4\" class=\"data row12 col4\" >3.62%</td>\n",
       "      <td id=\"T_baf03_row12_col5\" class=\"data row12 col5\" >3.50%</td>\n",
       "      <td id=\"T_baf03_row12_col6\" class=\"data row12 col6\" >3.45%</td>\n",
       "      <td id=\"T_baf03_row12_col7\" class=\"data row12 col7\" >4.20%</td>\n",
       "      <td id=\"T_baf03_row12_col8\" class=\"data row12 col8\" >4.14%</td>\n",
       "      <td id=\"T_baf03_row12_col9\" class=\"data row12 col9\" >4.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "      <td id=\"T_baf03_row13_col0\" class=\"data row13 col0\" >7.61%</td>\n",
       "      <td id=\"T_baf03_row13_col1\" class=\"data row13 col1\" >7.59%</td>\n",
       "      <td id=\"T_baf03_row13_col2\" class=\"data row13 col2\" >7.98%</td>\n",
       "      <td id=\"T_baf03_row13_col3\" class=\"data row13 col3\" >7.07%</td>\n",
       "      <td id=\"T_baf03_row13_col4\" class=\"data row13 col4\" >7.75%</td>\n",
       "      <td id=\"T_baf03_row13_col5\" class=\"data row13 col5\" >8.05%</td>\n",
       "      <td id=\"T_baf03_row13_col6\" class=\"data row13 col6\" >8.33%</td>\n",
       "      <td id=\"T_baf03_row13_col7\" class=\"data row13 col7\" >5.65%</td>\n",
       "      <td id=\"T_baf03_row13_col8\" class=\"data row13 col8\" >5.37%</td>\n",
       "      <td id=\"T_baf03_row13_col9\" class=\"data row13 col9\" >5.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "      <td id=\"T_baf03_row14_col0\" class=\"data row14 col0\" >-22.32%</td>\n",
       "      <td id=\"T_baf03_row14_col1\" class=\"data row14 col1\" >-22.03%</td>\n",
       "      <td id=\"T_baf03_row14_col2\" class=\"data row14 col2\" >-25.72%</td>\n",
       "      <td id=\"T_baf03_row14_col3\" class=\"data row14 col3\" >-17.63%</td>\n",
       "      <td id=\"T_baf03_row14_col4\" class=\"data row14 col4\" >-23.52%</td>\n",
       "      <td id=\"T_baf03_row14_col5\" class=\"data row14 col5\" >-26.57%</td>\n",
       "      <td id=\"T_baf03_row14_col6\" class=\"data row14 col6\" >-28.78%</td>\n",
       "      <td id=\"T_baf03_row14_col7\" class=\"data row14 col7\" >-4.82%</td>\n",
       "      <td id=\"T_baf03_row14_col8\" class=\"data row14 col8\" >-3.99%</td>\n",
       "      <td id=\"T_baf03_row14_col9\" class=\"data row14 col9\" >-6.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "      <td id=\"T_baf03_row15_col0\" class=\"data row15 col0\" >10.46%</td>\n",
       "      <td id=\"T_baf03_row15_col1\" class=\"data row15 col1\" >10.43%</td>\n",
       "      <td id=\"T_baf03_row15_col2\" class=\"data row15 col2\" >11.05%</td>\n",
       "      <td id=\"T_baf03_row15_col3\" class=\"data row15 col3\" >9.62%</td>\n",
       "      <td id=\"T_baf03_row15_col4\" class=\"data row15 col4\" >10.68%</td>\n",
       "      <td id=\"T_baf03_row15_col5\" class=\"data row15 col5\" >11.14%</td>\n",
       "      <td id=\"T_baf03_row15_col6\" class=\"data row15 col6\" >11.58%</td>\n",
       "      <td id=\"T_baf03_row15_col7\" class=\"data row15 col7\" >7.41%</td>\n",
       "      <td id=\"T_baf03_row15_col8\" class=\"data row15 col8\" >6.90%</td>\n",
       "      <td id=\"T_baf03_row15_col9\" class=\"data row15 col9\" >7.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "      <td id=\"T_baf03_row16_col0\" class=\"data row16 col0\" >6.95%</td>\n",
       "      <td id=\"T_baf03_row16_col1\" class=\"data row16 col1\" >6.89%</td>\n",
       "      <td id=\"T_baf03_row16_col2\" class=\"data row16 col2\" >7.40%</td>\n",
       "      <td id=\"T_baf03_row16_col3\" class=\"data row16 col3\" >6.32%</td>\n",
       "      <td id=\"T_baf03_row16_col4\" class=\"data row16 col4\" >7.10%</td>\n",
       "      <td id=\"T_baf03_row16_col5\" class=\"data row16 col5\" >7.55%</td>\n",
       "      <td id=\"T_baf03_row16_col6\" class=\"data row16 col6\" >7.81%</td>\n",
       "      <td id=\"T_baf03_row16_col7\" class=\"data row16 col7\" >4.60%</td>\n",
       "      <td id=\"T_baf03_row16_col8\" class=\"data row16 col8\" >4.67%</td>\n",
       "      <td id=\"T_baf03_row16_col9\" class=\"data row16 col9\" >5.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "      <td id=\"T_baf03_row17_col0\" class=\"data row17 col0\" >7.89%</td>\n",
       "      <td id=\"T_baf03_row17_col1\" class=\"data row17 col1\" >7.88%</td>\n",
       "      <td id=\"T_baf03_row17_col2\" class=\"data row17 col2\" >8.24%</td>\n",
       "      <td id=\"T_baf03_row17_col3\" class=\"data row17 col3\" >7.39%</td>\n",
       "      <td id=\"T_baf03_row17_col4\" class=\"data row17 col4\" >8.03%</td>\n",
       "      <td id=\"T_baf03_row17_col5\" class=\"data row17 col5\" >8.28%</td>\n",
       "      <td id=\"T_baf03_row17_col6\" class=\"data row17 col6\" >8.56%</td>\n",
       "      <td id=\"T_baf03_row17_col7\" class=\"data row17 col7\" >6.07%</td>\n",
       "      <td id=\"T_baf03_row17_col8\" class=\"data row17 col8\" >5.68%</td>\n",
       "      <td id=\"T_baf03_row17_col9\" class=\"data row17 col9\" >5.85%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_baf03_row18_col0\" class=\"data row18 col0\" >9.28%</td>\n",
       "      <td id=\"T_baf03_row18_col1\" class=\"data row18 col1\" >9.22%</td>\n",
       "      <td id=\"T_baf03_row18_col2\" class=\"data row18 col2\" >9.88%</td>\n",
       "      <td id=\"T_baf03_row18_col3\" class=\"data row18 col3\" >8.43%</td>\n",
       "      <td id=\"T_baf03_row18_col4\" class=\"data row18 col4\" >9.49%</td>\n",
       "      <td id=\"T_baf03_row18_col5\" class=\"data row18 col5\" >10.03%</td>\n",
       "      <td id=\"T_baf03_row18_col6\" class=\"data row18 col6\" >10.43%</td>\n",
       "      <td id=\"T_baf03_row18_col7\" class=\"data row18 col7\" >6.14%</td>\n",
       "      <td id=\"T_baf03_row18_col8\" class=\"data row18 col8\" >5.96%</td>\n",
       "      <td id=\"T_baf03_row18_col9\" class=\"data row18 col9\" >6.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "      <td id=\"T_baf03_row19_col0\" class=\"data row19 col0\" >6.59%</td>\n",
       "      <td id=\"T_baf03_row19_col1\" class=\"data row19 col1\" >6.56%</td>\n",
       "      <td id=\"T_baf03_row19_col2\" class=\"data row19 col2\" >6.99%</td>\n",
       "      <td id=\"T_baf03_row19_col3\" class=\"data row19 col3\" >6.05%</td>\n",
       "      <td id=\"T_baf03_row19_col4\" class=\"data row19 col4\" >6.73%</td>\n",
       "      <td id=\"T_baf03_row19_col5\" class=\"data row19 col5\" >7.08%</td>\n",
       "      <td id=\"T_baf03_row19_col6\" class=\"data row19 col6\" >7.34%</td>\n",
       "      <td id=\"T_baf03_row19_col7\" class=\"data row19 col7\" >4.56%</td>\n",
       "      <td id=\"T_baf03_row19_col8\" class=\"data row19 col8\" >4.42%</td>\n",
       "      <td id=\"T_baf03_row19_col9\" class=\"data row19 col9\" >4.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "      <td id=\"T_baf03_row20_col0\" class=\"data row20 col0\" >1.38%</td>\n",
       "      <td id=\"T_baf03_row20_col1\" class=\"data row20 col1\" >1.39%</td>\n",
       "      <td id=\"T_baf03_row20_col2\" class=\"data row20 col2\" >1.20%</td>\n",
       "      <td id=\"T_baf03_row20_col3\" class=\"data row20 col3\" >1.64%</td>\n",
       "      <td id=\"T_baf03_row20_col4\" class=\"data row20 col4\" >1.31%</td>\n",
       "      <td id=\"T_baf03_row20_col5\" class=\"data row20 col5\" >1.16%</td>\n",
       "      <td id=\"T_baf03_row20_col6\" class=\"data row20 col6\" >1.03%</td>\n",
       "      <td id=\"T_baf03_row20_col7\" class=\"data row20 col7\" >2.34%</td>\n",
       "      <td id=\"T_baf03_row20_col8\" class=\"data row20 col8\" >2.44%</td>\n",
       "      <td id=\"T_baf03_row20_col9\" class=\"data row20 col9\" >2.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "      <td id=\"T_baf03_row21_col0\" class=\"data row21 col0\" >-4.25%</td>\n",
       "      <td id=\"T_baf03_row21_col1\" class=\"data row21 col1\" >-4.14%</td>\n",
       "      <td id=\"T_baf03_row21_col2\" class=\"data row21 col2\" >-5.44%</td>\n",
       "      <td id=\"T_baf03_row21_col3\" class=\"data row21 col3\" >-2.61%</td>\n",
       "      <td id=\"T_baf03_row21_col4\" class=\"data row21 col4\" >-4.67%</td>\n",
       "      <td id=\"T_baf03_row21_col5\" class=\"data row21 col5\" >-5.75%</td>\n",
       "      <td id=\"T_baf03_row21_col6\" class=\"data row21 col6\" >-6.51%</td>\n",
       "      <td id=\"T_baf03_row21_col7\" class=\"data row21 col7\" >1.89%</td>\n",
       "      <td id=\"T_baf03_row21_col8\" class=\"data row21 col8\" >2.12%</td>\n",
       "      <td id=\"T_baf03_row21_col9\" class=\"data row21 col9\" >1.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "      <td id=\"T_baf03_row22_col0\" class=\"data row22 col0\" >4.76%</td>\n",
       "      <td id=\"T_baf03_row22_col1\" class=\"data row22 col1\" >4.73%</td>\n",
       "      <td id=\"T_baf03_row22_col2\" class=\"data row22 col2\" >4.93%</td>\n",
       "      <td id=\"T_baf03_row22_col3\" class=\"data row22 col3\" >4.53%</td>\n",
       "      <td id=\"T_baf03_row22_col4\" class=\"data row22 col4\" >4.81%</td>\n",
       "      <td id=\"T_baf03_row22_col5\" class=\"data row22 col5\" >5.01%</td>\n",
       "      <td id=\"T_baf03_row22_col6\" class=\"data row22 col6\" >5.09%</td>\n",
       "      <td id=\"T_baf03_row22_col7\" class=\"data row22 col7\" >3.87%</td>\n",
       "      <td id=\"T_baf03_row22_col8\" class=\"data row22 col8\" >4.02%</td>\n",
       "      <td id=\"T_baf03_row22_col9\" class=\"data row22 col9\" >4.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "      <td id=\"T_baf03_row23_col0\" class=\"data row23 col0\" >8.62%</td>\n",
       "      <td id=\"T_baf03_row23_col1\" class=\"data row23 col1\" >8.59%</td>\n",
       "      <td id=\"T_baf03_row23_col2\" class=\"data row23 col2\" >9.16%</td>\n",
       "      <td id=\"T_baf03_row23_col3\" class=\"data row23 col3\" >7.87%</td>\n",
       "      <td id=\"T_baf03_row23_col4\" class=\"data row23 col4\" >8.82%</td>\n",
       "      <td id=\"T_baf03_row23_col5\" class=\"data row23 col5\" >9.27%</td>\n",
       "      <td id=\"T_baf03_row23_col6\" class=\"data row23 col6\" >9.65%</td>\n",
       "      <td id=\"T_baf03_row23_col7\" class=\"data row23 col7\" >5.85%</td>\n",
       "      <td id=\"T_baf03_row23_col8\" class=\"data row23 col8\" >5.55%</td>\n",
       "      <td id=\"T_baf03_row23_col9\" class=\"data row23 col9\" >5.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_baf03_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "      <td id=\"T_baf03_row24_col0\" class=\"data row24 col0\" >8.02%</td>\n",
       "      <td id=\"T_baf03_row24_col1\" class=\"data row24 col1\" >7.92%</td>\n",
       "      <td id=\"T_baf03_row24_col2\" class=\"data row24 col2\" >8.81%</td>\n",
       "      <td id=\"T_baf03_row24_col3\" class=\"data row24 col3\" >6.94%</td>\n",
       "      <td id=\"T_baf03_row24_col4\" class=\"data row24 col4\" >8.29%</td>\n",
       "      <td id=\"T_baf03_row24_col5\" class=\"data row24 col5\" >9.07%</td>\n",
       "      <td id=\"T_baf03_row24_col6\" class=\"data row24 col6\" >9.52%</td>\n",
       "      <td id=\"T_baf03_row24_col7\" class=\"data row24 col7\" >3.93%</td>\n",
       "      <td id=\"T_baf03_row24_col8\" class=\"data row24 col8\" >4.09%</td>\n",
       "      <td id=\"T_baf03_row24_col9\" class=\"data row24 col9\" >4.71%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x16976b410>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "6e72041bc49a6ff39e74ccd56b9391c544b799a0d2a04160b530f8b1ce965df8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
